Senior Software Engineer, Network Platform

🏢 Summary: Engineering role focused on building a unified infrastructure platform that bridges bare-metal servers, GPU clusters, storage, and networking into a programmable system for large-scale AI and HPC workloads. The position involves designing orchestration layers, automation frameworks, APIs, and observability systems to manage compute, storage, and networking at scale. The goal is to deliver high-performance, compliant, and enterprise-grade AI infrastructure for demanding research and production environments. 🗂️ Requirements: 5+ years software engineering experience in infrastructure platforms, distributed systems, or cloud platforms, Strong Kubernetes knowledge and container orchestration experience, Deep understanding of compute, storage, and networking infrastructure, Experience with systems programming languages (Go, C/C++, Rust, Python), Strong Linux production experience, Knowledge of bare-metal provisioning and virtualization technologies, Experience designing APIs, SDKs, and automation frameworks, Familiarity with AWS, GCP, or Azure, Experience with Infrastructure-as-Code tools, Experience with network automation and high-performance networking concepts, Understanding of observability, telemetry, and logging systems, Knowledge of enterprise security and compliance standards (SOC2, ISO27001) 📃 Skills: Go, Python, Kubernetes, Docker, Terraform, Ansible, Linux, KVM, SLURM, AWS, GCP, Azure, BGP, VXLAN, InfiniBand, RDMA, SR-IOV, FastAPI, PostgreSQL, Redis, Prometheus, Grafana, ELK, NVIDIA 🏢 Description: Moonlite delivers high-performance AI infrastructure for organizations running intensive computational research, large-scale model training, and demanding data processing workloads.We provide infrastructure deployed in our facilities or co-located in yours, delivering flexible on-demand or reserved compute that feels like an extension of your existing data center. Our team of AI infrastructure specialists combines bare-metal performance with cloud-native operational simplicity, enabling research teams and enterprises to deploy demanding AI workloads with enterprise-grade reliability and compliance. Your Role: You will be foundational to building the comprehensive infrastructure platform that bridges our physical infrastructure – bare-metal servers, GPU clusters, high-performance storage, and networking fabric – with the systems our customers depend on for large-scale computation, inference, simulations, and training. Working closely with product, your platform team members, and infrastructure specialists, you'll design and implement the orchestration layer, APIs, and automation framework that make thousands of servers, petabytes of storage, and high-speed networks feel like a unified, programmable platform. Job Responsibilities Infrastructure Abstraction Layer: Design and build systems that bridge physical infrastructure (bare-metal servers, storage clusters, network fabric) with customer-facing services, enabling programmatic management of compute, networking, and storage at scale. Research Cluster Provisioning: Design and implement systems for provisioning and managing research computing environments including Kubernetes and SLURM clusters, enabling automated deployment, resource scheduling, and workload orchestration for distributed AI training and HPC workloads. Platform Orchestration: Implement comprehensive orchestration systems that coordinate across compute, storage and networking to deliver unified experience for complex research workloads. Network Automation & Placement: Design and build network provisioning automation including intelligent VM placement decisions for optimal network topology, automated VLAN and subnet configuration, and software-designed networking orchestration for high-performance interconnects. Enterprise APIs & SDKs: Develop robust APIs and SDKs that enable researchers and engineering teams to programmatically provision and manage infrastructure resources across all platform domains. Observability & Telemetry: Implement comprehensive observability, telemetry, and logging systems that provide visibility into infrastructure health, performance, and utilization across the infrastructure footprint. Performance Engineering: Build and optimize platform services that deliver consistent high-throughput low-latency networking for demand research applications and data-intensive workloads. Cross-Team Collaboration: Work closely with engineering, infrastructure, and product to define requirements, drive infrastructure-product-rollouts, and improve resource lifecycle management. Compliance & Security: Implement platform-wide compliance and security features supporting SOC 2, ISO 27001, and enterprise regulatory requirements including comprehensive audit logging, access controls, and data residency management. Requirements Experience: 5+ years in software engineering with a proven track record of infrastructure platforms, distributed systems, or cloud platforms for production environments. Kubernetes & Container Orchestration: Strong familiarity with Kubernetes architecture, container orchestration concepts, and experience deploying workloads in Kubernetes environments. Understanding of pods, deployments, services, and basic Kubernetes operations. Infrastructure Systems: Strong understanding of infrastructure fundamentals including compute orchestration, storage systems, networking technologies, and how they integrate together to deliver complete platform experiences. Programming Skills: Experience with systems programming languages (Go, C/C++, Rust, Python) for performance-critical components is a strong plus. Linux Production Experience: Strong experience with linux in production environments, including systems administration, performance tuning, and troubleshooting. Bare-Metal & Virtualization: Deep knowledge of bare-metal infrastructure, provisioning systems, out-of-band management, and virtualization technologies (KVM, Kubernetes, etc). API & Platform Design: Proven experience designing and building APIs, SDKs, and automation frameworks that enable programmatic infrastructure management. Cloud Platform Knowledge: Strong familiarity with cloud environments (AWS, GCP, Azure) and understanding of how to translate cloud-native patterns to bare-metal infrastructure. Infrastructure Automation: Experience with Infrastructure-as-code tools (Terraform, Ansible) and building automated deployment pipelines. Problem Solving & Autonomy: Self-starter who can navigate ambiguity, balance pragmatic shipping with good long-term architecture, and independently drive complex technical initiatives. Communication Skills: Strong written and verbal communication skills, including ability to write clear technical communication and collaborate across teams. Commitment to Growth: Growth mindset with continuous focus on learning and professional development. Preferred Qualifications Background provisioning or managing research computing environments (Kubernetes, SLURM, or HPC clusters) Experience building internal platforms, infrastructure-as-a-service, or developer tooling Background with GPU computing platforms and AI/ML infrastructure requirements Knowledge of high-performance networking technologies (InfiniBand, RDMA, SR-IOV) Experience with observability and monitoring platforms (Prometheus, Grafana, ELK stack) Familiarity with both cloud-native and bare-metal infrastructure deployment models Understanding of enterprise compliance requirements and security best practices Extra points for experience with financial services technology infrastructure and understanding of trading system requirements Key Technologies Go, Python, Kubernetes, Docker, Terraform, Ansible, Linux, Networking (BGP, VXLAN), Storage Systems, FastAPI, PostgreSQL, Redis, NVIDIA GPU Technologies, InfiniBand Why Moonlite Build Next-Generation Infrastructure: Your work will create the platform foundation that enables financial institutions to harness AI capabilities previously impossible with traditional infrastructure. Hands-On Ownership: As an early engineer, you'll have end-to-end ownership of projects and the autonomy to influence our product and technology direction. Shape Industry Standards: Contribute to defining how enterprise AI infrastructure should work for the most demanding regulated environments. Collaborate with Experts: Work alongside seasoned engineers and industry professionals passionate about high-performance computing, innovation, and problem-solving. Start-Up Agility with Industry Impact: Enjoy the dynamic, fast-paced environment of a startup while making an immediate impact in an evolving and critical technology space. We offer a competitive total compensation package combining a competitive base salary, startup equity, and industry-leading benefits. The total compensation range for this role is $165,000 – $225,000, which includes both base salary and equity. Actual compensation will be determined based on experience, skills, and market alignment. We provide generous benefits, including a 6% 401(k) match, fully covered health insurance premiums, and other comprehensive offerings to support your well-being and success as we grow together. #li-remote

🏢 Summary: The role focuses on designing, building, and operating production-grade Kubernetes infrastructure on bare-metal to power large-scale AI and high-performance compute workloads. It involves deep integration of networking, storage, and GPU technologies while ensuring enterprise-grade reliability, automation, and observability across multi-region environments. The position emphasizes Kubernetes internals, infrastructure automation, and SRE practices for mission-critical AI systems. 🗂️ Requirements: 5+ years experience in SRE, DevOps, or infrastructure engineering operating production infrastructure at scale, Hands-on experience building and operating Kubernetes clusters on bare-metal infrastructure, Strong knowledge of Kubernetes internals including CRDs, operators, controllers, admission webhooks, and scheduling, Experience integrating CSI storage, CNI networking, SR-IOV, and GPU device plugins with Kubernetes, Strong Linux systems administration and troubleshooting skills, Proficiency with infrastructure-as-code tools for automation, Solid understanding of networking fundamentals (IPAM, DNS, DHCP, VLAN, VXLAN, routing, load balancing), Experience implementing monitoring and logging solutions in production environments, Understanding of SRE principles including SLIs, SLOs, SLAs, and incident management, Strong scripting skills in Go, Python, or Bash 📃 Skills: Kubernetes, Linux, Terraform, Ansible, Helm, Prometheus, Grafana, ELK, Go, Python, Bash, NVIDIA, SR-IOV, CSI, CNI, Calico, Cilium, Multus, Ceph, InfiniBand, RDMA, RoCE, etcd 🏢 Description: Moonlite delivers high-performance AI infrastructure for organizations running intensive computational research, large-scale model training, and demanding data processing workloads.We provide infrastructure deployed in our facilities or co-located in yours, delivering flexible on-demand or reserved compute that feels like an extension of your existing data center. Our team of AI infrastructure specialists combines bare-metal performance with cloud-native operational simplicity, enabling research teams and enterprises to deploy demanding AI workloads with enterprise-grade reliability and compliance. Your Role: You will be instrumental in building and operating production-grade AI infrastructure with deep Kubernetes expertise at its core. Working closely with our systems engineers, network engineers, and platform engineering team, you'll architect and operate the Kubernetes infrastructure that powers our control plane and orchestrates compute, storage, and networking at scale. This role requires deep understanding of Kubernetes internals, custom resource definitions (CRDs), storage and network integrations, and building production-grade clusters from the ground up (not just deploying in managed environments). You'll ensure enterprise-grade reliability while establishing the automation, observability, and operational practices. Job Responsibilities Kubernetes Infrastructure Engineering: Design, build, and operate production Kubernetes clusters on bare-metal infrastructure – including cluster bootstrapping, control plane architecture, etcd management, and scaling strategies for high-performance compute workloads. Kubernetes Networking & CNIs: Implement and operate custom Kubernetes networking solutions with SR-IOV for high-performance GPU interconnects, multi-tenancy isolation and advanced networking policies. Configure CNI plugins and network segmentation for research workloads. Custom Operators & Controllers: Develop and maintain custom Kubernetes operators and controllers for bare-metal provisioning, infrastructure lifecycle management, and resource orchestration across compute, storage, and networking domains. GPU Infrastructure Integration: Deploy and optimize NVIDIA GPU operators, device plugins, and other custom scheduling logic for GPU workload placement and utilization optimization. Platform Integration & Storage: Build deep integrations between Kubernetes and underlying infrastructure including CSI drivers for storage, custom admission controllers for policy enforcement, and scheduling extensions for specialized hardware placement. Infrastructure Automation: Design and implement automation using Terraform, Ansible, Helm, and custom operators to orchestrate infrastructure workflows and enable deployments across multiple regions. Production Operations & Reliability: Manage production bare-metal infrastructure across multiple regions. Build systems ensuring high availability, fault tolerance, and graceful degradation – establishing SLIs, SLOs, and monitoring to meet enterprise reliability commitments. Observability & Incident Response: Build comprehensive monitoring, logging, and alerting using Prometheus, Grafana, and ELK stack. Lead incident response, conduct postmortems, and implement preventative measures to improve reliability and reduce MTTR. Performance & Capacity Planning: Identify and resolve performance bottlenecks across infrastructure domains. Monitor utilization trends, forecast capacity needs, and optimize resource allocation for various workloads. Requirements Experience: 5+ years in SRE, DevOps, or infrastructure engineering roles with proven experience operating production infrastructure at scale. Kubernetes Infrastructure Expertise: Deep hands-on experience building and operating production Kubernetes clusters on bare-metal infrastructure – not just deploying workloads in managed clusters. Must understand cluster bootstrapping, control plane architecture, etcd operations, and scaling strategies. Kubernetes Internals & Integration: Strong understanding of Kubernetes internals including custom resource definitions (CRDs), operators, controllers, admission webhooks, and scheduling. Experience integrating storage (CSI drivers), networking (CNI, SR-IOV), and specialized hardware (GPU device plugins) with Kubernetes. Linux Systems Experience: Strong fundamentals in Linux systems administration, performance tuning, troubleshooting, and automation in production environments. Infrastructure Automation: Proficiency with infrastructure-as-code tools (Terraform, Ansible, Helm) and building automation to reduce operational overhead. Networking Fundamentals: Solid understanding of networking concepts including IPAM, DNS, DHCP, VLAN/VXLAN, routing, load balancing, and experience troubleshooting network issues in production. Observability & Monitoring: Experience building and maintaining comprehensive monitoring solutions using tools like Prometheus, Grafana, and centralized logging systems. Reliability Practices: Understanding of SRE principles including SLIs/SLOs/SLAs, error budgets, incident management, and blameless postmortems. Scripting & Automation: Strong scripting skills in Go, Python, or Bash for automation, tooling development, and operational efficiency. Problem-Solving Under Pressure: Demonstrated ability to troubleshoot complex issues under pressure, manage incidents effectively, and communicate clearly during outages. Collaboration & Communication: Excellent communication skills and ability to work across teams including systems engineers, network engineers, and software developers. Preferred Qualifications Experience building custom Kubernetes operators or controllers for infrastructure orchestration Deep familiarity with Kubernetes networking (Calico, Cilium, Multus), service mesh technologies, and network policy management Experience with GPU workload orchestration including NVIDIA GPU Operator, MIG, time-slicing, and device plugins Background with advanced Kubernetes features including custom schedulers, admission controllers, and API server extensions Experience with Kubernetes cluster federation or multi-cluster management Knowledge of high-performance networking technologies (InfiniBand, RDMA, RoCE) and their integration with Kubernetes Experience with enterprise storage systems (VAST, Lightbits, Ceph, or similar) Familiarity with configuration management at scale and GitOps practices Understanding of security best practices for Kubernetes and bare-metal infrastructure Experience operating infrastructure in regulated industries or co-located data center environments Background supporting research institutions, technical computing environments, or enterprise AI infrastructure Key Technologies Kubernetes, Linux, Terraform, Ansible, Prometheus, Grafana, ELK Stack, Go, Python, Bash, NVIDIA GPU Technologies, High-Performance Networking, Enterprise Storage Systems Why Moonlite Build Critical Research Infrastructure: Your work will directly enable quantitative research teams and AI practitioners to push the boundaries of what's possible in financial modeling and AI research. Enterprise Impact: Build and operate infrastructure that supports mission-critical research and AI workloads for leading financial institutions and research organizations. Technical Excellence: Join an infrastructure team focused on delivering enterprise-grade reliability while pushing the boundaries of high-performance computing capabilities. Hands-On Ownership: As part of our growing infrastructure team, you'll have significant ownership over critical systems and the autonomy to influence our operational practices and technology choices. Industry Leadership: Work alongside experienced infrastructure professionals who have built and operated systems for the most demanding computing environments. We offer a competitive total compensation package combining a competitive base salary, startup equity, and industry-leading benefits. The total compensation range for this role is $165,000 – $225,000, which includes both base salary and equity. Actual compensation will be determined based on experience, skills, and market alignment. We provide generous benefits, including a 6% 401(k) match, fully covered health insurance premiums, and other comprehensive offerings to support your well-being and success as we grow together. #li-remote

🏢 Summary: Design and build a GPU-accelerated compute orchestration platform for distributed AI training, inference, and large-scale research workloads. The role focuses on managing GPU clusters, bare-metal provisioning, and intelligent resource scheduling with cloud-like simplicity. You will develop scalable, high-performance systems enabling programmatic access to multi-tenant AI infrastructure. 🗂️ Requirements: 5+ years software engineering experience, Experience building compute orchestration or distributed compute systems, Strong Kubernetes knowledge and workload deployment experience, Proficiency in Go, C/C++, Python, or Rust, Strong Linux systems programming experience, Experience with virtualization and container technologies, Understanding of GPU architectures and resource management, Experience with bare-metal provisioning and lifecycle management, Ability to design scalable, high-performance systems 📃 Skills: Go, C++, Python, Rust, Kubernetes, Docker, KVM, Xen, CUDA, NVIDIA, GPUDirect, SR-IOV, vGPU, InfiniBand, RDMA, NVLink, NVSwitch, Terraform, FastAPI, gRPC, Linux, SLURM 🏢 Description: Moonlite delivers high-performance AI infrastructure for organizations running intensive computational research, large-scale model training, and demanding data processing workloads.We provide infrastructure deployed in our facilities or co-located in yours, delivering flexible on-demand or reserved compute that feels like an extension of your existing data center. Our team of AI infrastructure specialists combines bare-metal performance with cloud-native operational simplicity, enabling research teams and enterprises to deploy demanding AI workloads with enterprise-grade reliability and compliance. Your Role: You will be instrumental in building out our GPU-accelerated compute platform that powers distributed AI training and inference, large-scale simulations, and computational research workloads. Working closely with product, your platform team members, and infrastructure specialists, you'll design and implement the compute orchestration layer that manages GPU clusters, bare-metal provisioning, and resource scheduling-enabling researchers and engineers to programmatically access high-performance compute resources with cloud-like simplicity. Job Responsibilities Compute Orchestration Systems: Design and build scalable compute orchestration platforms that manage GPU clusters, bare-metal server provisioning, and resource allocation across co-located infrastructure environments. Resource Management & Scheduling: Implement intelligent workload scheduling, resource allocation, and optimization algorithms that maximize GPU utilization while maintaining performance guarantees for research and training workloads. Research Cluster Provisioning: Design and implement systems for provisioning and managing research computing environments including Kubernetes and SLURM clusters, enabling automated deployment, resource scheduling, and workload orchestration for distributed AI training and HPC workloads. GPU Platform Engineering: Develop platform capabilities for managing latest-generation NVIDIA GPU configurations (H100, H200, B200, B300), including GPU resource management, multi-tenant isolation, and integration with compute orchestration systems. Bare-Metal Lifecycle Management: Build automation and tooling for complete bare-metal server lifecycle management – from initial provisioning and configuration through ongoing operations, updates, and resource reallocation. Performance-Critical Systems: Optimize compute platform components for high-throughput and low-latency performance, ensuring research workloads achieve near-bare-metal efficiency in virtualized or containersized environments. Platform APIs & Integration: Develop robust APIs and SDKs that enable researchers to programmatically provision and manage compute resources, integrating seamlessly with existing workflows and research infrastructure. Observability & Monitoring: Implement comprehensive monitoring and telemetry systems for compute resources, providing visibility into GPU virtualization, workload performance and infrastructure health. Multi-Tenancy and Isolation: Build enterprise-grade multi-tenant compute isolation, security boundaries, and resource quotas that enable safe sharing of GPU infrastructure across teams and organizations. Requirements Experience: 5+ years in software engineering with proven experience building compute platforms, container orchestration systems, or distributed compute infrastructure for production environments. Compute Platform Engineering: Strong background in building compute orchestration, resource scheduling, or workload management systems at scale. Kubernetes & Container Orchestration: Strong familiarity with Kubernetes architecture, container orchestration concepts, and experience deploying workloads in Kubernetes environments. Understanding of pods, deployments, services, and basic Kubernetes operations. Programming Skills: Experience with Go, C/C++, Python, or Rust for performance-critical components is highly valued. Linux & Systems Programming: Strong experience with Linux in production environments, including systems for programming, performance optimization, and low-level resource management. Virtualization & Containers: Deep knowledge of virtualization technologies (KVM, Xen), container runtimes, and orchestration platforms. GPU Computing Fundamentals: Understanding of GPU architectures, CUDA programming (where/when needed), and GPU resource management – or a strong ability to learn quickly. Bare-Metal Infrastructure: Experience with bare-metal provisioning, out-of-band management systems, and hardware abstraction layers. Problem-Solving & Architecture: Demonstrated ability to solve complex performance and scalability challenges while balancing pragmatic shipping with good long-term architecture. Autonomy & Communication: Comfortable navigating ambiguity, defining requirements collaboratively, and communicating technical discussions through clear documentation. Commitment to Growth: Growth mindset with continuous focus on learning and professional development. Preferred Qualifications Background provisioning or managing research computing environments (Kubernetes, SLURM, or HPC clusters) Experience with GPU virtualization technologies (SR-IOV, NVIDIA vGPU) and multi-tenant GPU sharing Background in container orchestration platforms with custom scheduling or resource management Knowledge of high-performance networking for GPU communication (InfiniBand, RDMA, NVLink, NVSwitch) Familiarity with AI/ML training frameworks (PyTorch, TensorFlow) and their infrastructure requirements Understanding of distributed training patterns and multi-node GPU coordination Experience building infrastructure for research institutions,labs, or technical computing environments Background in financial services or other regulated industry infrastructure is a plus Key Technologies Go, C/C++, Python, KVM, Docker, Kubernetes,, NVIDIA GPUDirect, SR-IOV, NVIDIA vGPU, CUDA, InfiniBand, RDMA, Terraform, FastAPI, gRPC, Linux systems programming Why Moonlite Build Next-Generation Infrastructure: Your work will create the platform foundation that enables financial institutions to harness AI capabilities previously impossible with traditional infrastructure. Hands-On Ownership: As an early engineer, you'll have end-to-end ownership of projects and the autonomy to influence our product and technology direction. Shape Industry Standards: Contribute to defining how enterprise AI infrastructure should work for the most demanding regulated environments. Collaborate with Experts: Work alongside seasoned engineers and industry professionals passionate about high-performance computing, innovation, and problem-solving. Start-Up Agility with Industry Impact: Enjoy the dynamic, fast-paced environment of a startup while making an immediate impact in an evolving and critical technology space. We offer a competitive total compensation package combining a competitive base salary, startup equity, and industry-leading benefits. The total compensation range for this role is $165,000 – $225,000, which includes both base salary and equity. Actual compensation will be determined based on experience, skills, and market alignment. We provide generous benefits, including a 6% 401(k) match, fully covered health insurance premiums, and other comprehensive offerings to support your well-being and success as we grow together. #li-remote

🏢 Summary: Engineering role focused on designing and building a GPU-accelerated compute orchestration platform for distributed AI training, inference, and large-scale research workloads. The position involves developing systems for GPU cluster management, bare-metal provisioning, intelligent scheduling, and multi-tenant resource isolation. You will create high-performance, cloud-like infrastructure enabling programmatic access to scalable compute resources. 🗂️ Requirements: 5+ years software engineering experience in compute platforms or distributed infrastructure, Experience building compute orchestration or resource scheduling systems at scale, Strong knowledge of Kubernetes architecture and container orchestration, Proficiency in Go, C/C++, Python, or Rust, Strong Linux systems programming experience, Experience with virtualization technologies and container runtimes, Understanding of GPU architectures and CUDA, Experience with bare-metal provisioning and lifecycle management 📃 Skills: Go, C++, Python, Rust, Kubernetes, Docker, KVM, Xen, CUDA, Linux, SLURM, Terraform, gRPC, FastAPI, InfiniBand, RDMA, NVLink, NVSwitch, GPUDirect, SR-IOV, vGPU 🏢 Description: Moonlite delivers high-performance AI infrastructure for organizations running intensive computational research, large-scale model training, and demanding data processing workloads.We provide infrastructure deployed in our facilities or co-located in yours, delivering flexible on-demand or reserved compute that feels like an extension of your existing data center. Our team of AI infrastructure specialists combines bare-metal performance with cloud-native operational simplicity, enabling research teams and enterprises to deploy demanding AI workloads with enterprise-grade reliability and compliance. Your Role: You will be instrumental in building out our GPU-accelerated compute platform that powers distributed AI training and inference, large-scale simulations, and computational research workloads. Working closely with product, your platform team members, and infrastructure specialists, you'll design and implement the compute orchestration layer that manages GPU clusters, bare-metal provisioning, and resource scheduling-enabling researchers and engineers to programmatically access high-performance compute resources with cloud-like simplicity. Job Responsibilities Compute Orchestration Systems: Design and build scalable compute orchestration platforms that manage GPU clusters, bare-metal server provisioning, and resource allocation across co-located infrastructure environments. Resource Management & Scheduling: Implement intelligent workload scheduling, resource allocation, and optimization algorithms that maximize GPU utilization while maintaining performance guarantees for research and training workloads. Research Cluster Provisioning: Design and implement systems for provisioning and managing research computing environments including Kubernetes and SLURM clusters, enabling automated deployment, resource scheduling, and workload orchestration for distributed AI training and HPC workloads. GPU Platform Engineering: Develop platform capabilities for managing latest-generation NVIDIA GPU configurations (H100, H200, B200, B300), including GPU resource management, multi-tenant isolation, and integration with compute orchestration systems. Bare-Metal Lifecycle Management: Build automation and tooling for complete bare-metal server lifecycle management – from initial provisioning and configuration through ongoing operations, updates, and resource reallocation. Performance-Critical Systems: Optimize compute platform components for high-throughput and low-latency performance, ensuring research workloads achieve near-bare-metal efficiency in virtualized or containersized environments. Platform APIs & Integration: Develop robust APIs and SDKs that enable researchers to programmatically provision and manage compute resources, integrating seamlessly with existing workflows and research infrastructure. Observability & Monitoring: Implement comprehensive monitoring and telemetry systems for compute resources, providing visibility into GPU virtualization, workload performance and infrastructure health. Multi-Tenancy and Isolation: Build enterprise-grade multi-tenant compute isolation, security boundaries, and resource quotas that enable safe sharing of GPU infrastructure across teams and organizations. Requirements Experience: 5+ years in software engineering with proven experience building compute platforms, container orchestration systems, or distributed compute infrastructure for production environments. Compute Platform Engineering: Strong background in building compute orchestration, resource scheduling, or workload management systems at scale. Kubernetes & Container Orchestration: Strong familiarity with Kubernetes architecture, container orchestration concepts, and experience deploying workloads in Kubernetes environments. Understanding of pods, deployments, services, and basic Kubernetes operations. Programming Skills: Experience with Go, C/C++, Python, or Rust for performance-critical components is highly valued. Linux & Systems Programming: Strong experience with Linux in production environments, including systems for programming, performance optimization, and low-level resource management. Virtualization & Containers: Deep knowledge of virtualization technologies (KVM, Xen), container runtimes, and orchestration platforms. GPU Computing Fundamentals: Understanding of GPU architectures, CUDA programming (where/when needed), and GPU resource management – or a strong ability to learn quickly. Bare-Metal Infrastructure: Experience with bare-metal provisioning, out-of-band management systems, and hardware abstraction layers. Problem-Solving & Architecture: Demonstrated ability to solve complex performance and scalability challenges while balancing pragmatic shipping with good long-term architecture. Autonomy & Communication: Comfortable navigating ambiguity, defining requirements collaboratively, and communicating technical discussions through clear documentation. Commitment to Growth: Growth mindset with continuous focus on learning and professional development. Preferred Qualifications Background provisioning or managing research computing environments (Kubernetes, SLURM, or HPC clusters) Experience with GPU virtualization technologies (SR-IOV, NVIDIA vGPU) and multi-tenant GPU sharing Background in container orchestration platforms with custom scheduling or resource management Knowledge of high-performance networking for GPU communication (InfiniBand, RDMA, NVLink, NVSwitch) Familiarity with AI/ML training frameworks (PyTorch, TensorFlow) and their infrastructure requirements Understanding of distributed training patterns and multi-node GPU coordination Experience building infrastructure for research institutions,labs, or technical computing environments Background in financial services or other regulated industry infrastructure is a plus Key Technologies Go, C/C++, Python, KVM, Docker, Kubernetes,, NVIDIA GPUDirect, SR-IOV, NVIDIA vGPU, CUDA, InfiniBand, RDMA, Terraform, FastAPI, gRPC, Linux systems programming Why Moonlite Build Next-Generation Infrastructure: Your work will create the platform foundation that enables financial institutions to harness AI capabilities previously impossible with traditional infrastructure. Hands-On Ownership: As an early engineer, you'll have end-to-end ownership of projects and the autonomy to influence our product and technology direction. Shape Industry Standards: Contribute to defining how enterprise AI infrastructure should work for the most demanding regulated environments. Collaborate with Experts: Work alongside seasoned engineers and industry professionals passionate about high-performance computing, innovation, and problem-solving. Start-Up Agility with Industry Impact: Enjoy the dynamic, fast-paced environment of a startup while making an immediate impact in an evolving and critical technology space. We offer a competitive total compensation package combining a competitive base salary, startup equity, and industry-leading benefits. The total compensation range for this role is $165,000 – $225,000, which includes both base salary and equity. Actual compensation will be determined based on experience, skills, and market alignment. We provide generous benefits, including a 6% 401(k) match, fully covered health insurance premiums, and other comprehensive offerings to support your well-being and success as we grow together. #li-remote

🏢 Summary: The offer is for a Senior Platform Engineer responsible for designing, building, and operating scalable Kubernetes-based platforms on AWS. The role focuses on backend platform development, Infrastructure as Code with Terraform, CI/CD automation, and improving developer experience through self-service and multi-tenant solutions. It involves driving reliability, observability, and automation across a cloud-native ecosystem. 🗂️ Requirements: 5+ years of experience as a Platform Engineer, Strong hands-on experience with Kubernetes, including cluster operations, Strong programming skills in TypeScript or Go, Experience with AWS or other cloud platforms, Experience with Infrastructure as Code tools, preferably Terraform, Experience with CI/CD pipelines and deployment automation, Experience designing scalable multi-tenant systems, Strong understanding of API and platform architecture, Solid Linux and networking fundamentals, Experience with observability and monitoring solutions, Experience building self-service platform capabilities, Practical experience using AI-powered coding assistants, Work permit and residence in the European Union 📃 Skills: Kubernetes, AWS, Terraform, TypeScript, Go, Python, CI/CD, Linux, Networking, APIs, Docker, OpenTelemetry, Backstage, GitHub, Claude, Cursor 🏢 Description: 🟣 You will be: designing, building, and operating scalable Kubernetes-based platform infrastructure, developing backend platform services and reusable engineering components, building and maintaining cloud-native solutions (primarily on AWS), creating and evolving Infrastructure as Code solutions using Terraform, designing and implementing CI/CD pipelines and deployment automation, developing scalable multi-tenant platform capabilities for engineering teams, building self-service developer platform features and internal tooling, designing and maintaining APIs and platform architecture standards, improving platform reliability, observability, and monitoring capabilities, collaborating with engineering teams to improve developer experience and platform adoption, contributing to platform engineering best practices and technical standards, supporting container platform operations and orchestration processes, driving automation and operational excellence across the platform ecosystem. 🟣 Your profile: 5+ years of experience as a Platform Engineer, practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery, strong hands-on experience with Kubernetes, including cluster operations and orchestration concepts, strong backend engineering background, strong programming skills in TypeScript and/or Go; Python experience is a plus, solid experience with cloud platforms, preferably AWS, practical experience with Infrastructure as Code tools, preferably Terraform, experience with CI/CD pipelines and deployment orchestration, experience designing scalable multi-tenant systems, strong understanding of API and platform architecture, solid Linux and networking fundamentals, experience with observability and monitoring solutions, experience building reusable self-service platform capabilities for developers, good English communication skills (at least B2 level). 🟣 Nice to have: experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work, interest in and familiarity with emerging AI-driven practices (e.g. agent-based workflows, automation patterns, AI-augmented development), with a willingness to explore and experiment beyond standard approaches, experience with Internal Developer Platforms (IDP), experience building Kubernetes operators and controllers, experience with Backstage or developer portal tooling, experience in container runtime or platform engineering, knowledge of event-driven and distributed systems architecture, experience with service mesh technologies and OpenTelemetry. Work from the European Union region and a work permit are required. 🟣 Recruitment Process: CV review – HR Call – Interview – Client Interview – Decision 🎁 Benefits 🎁 ✍ Development: development budget of up to 6,800 PLN, we fund certifications e.g.: AWS, Azure, ISTQB, PSM, access to Udemy, Safari Books Online and more, events and technology conferences, technology Guilds, internal training, Xebia Library, Xebia Upskill. 🩺 We take care of your health: private medical healthcare, multiSport card - we subsidise a MultiSport card, mental Health Support. 🤸‍♂️ We are flexible: flexible working hours, B2B or permanent contract, contract for an indefinite period.

🏢 Summary: Senior Network Engineer role focused on designing, deploying, automating, and operating large-scale NVIDIA InfiniBand networking infrastructure for AI GPU clusters. The position involves managing high-performance AI fabrics, optimizing distributed AI workloads, and building scalable, highly available AI Factory environments using modern data center networking technologies. 🗂️ Requirements: 7+ years data center networking experience, 3+ years supporting NVIDIA InfiniBand environments, Hands-on experience with NVIDIA UFM Enterprise, Experience with Quantum and Quantum-2 InfiniBand switches, Strong knowledge of InfiniBand Architecture, Linux administration experience, Automation experience with Python and Ansible, Understanding of Layer 2 and Layer 3 networking, Experience with BGP, Experience with EVPN, Experience with VXLAN, Experience troubleshooting with tcpdump, Experience troubleshooting with Wireshark, Experience troubleshooting with ibdiagnet, Experience with spine-leaf architectures, Experience operating HPC or GPU-dense environments 📃 Skills: InfiniBand, UFM, NCCL, RoCEv2, Quantum, Linux, Ubuntu, Python, Ansible, Git, REST, Prometheus, Grafana, BGP, EVPN, VXLAN, tcpdump, Wireshark, ibdiagnet, MPI, GPUDirect, RDMA, Terraform, Netris 🏢 Description: Who We Are Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction. Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in. We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute. The Role We are seeking an experienced Senior Network Engineer (InfiniBand / UFM) to design, deploy, automate, and operate next-generation AI Factory networking infrastructure supporting large-scale GPU clusters. This role is responsible for building and maintaining high-performance NVIDIA Quantum InfiniBand fabrics that power AI training and inference environments utilizing NVIDIA UFM, NCCL, RoCEv2, and modern data center technologies. The ideal candidate has deep expertise in InfiniBand networking, NVIDIA Unified Fabric Manager (UFM), large-scale GPU deployments, Linux networking, automation, and troubleshooting distributed AI workloads. What You'll Do - Design, deploy, and maintain large-scale NVIDIA InfiniBand fabrics supporting AI/ML GPU clusters. - Deploy and administer NVIDIA Unified Fabric Manager (UFM) Enterprise for monitoring, provisioning, telemetry, and fabric health. - Configure and optimize NVIDIA Quantum and Quantum-2 InfiniBand switches. - Troubleshoot fabric performance issues impacting NCCL, MPI, GPUDirect RDMA, and AI training jobs. - Implement and validate fat-tree, Dragonfly+, Clos, and spine-leaf network architectures. - Perform firmware lifecycle management for InfiniBand switches, adapters (HCAs), and UFM infrastructure. - Optimize congestion control, adaptive routing, QoS, and traffic engineering for high-performance GPU communication. - Work closely with AI platform, GPU infrastructure, storage, and systems engineering teams to deploy scalable AI Factory environments. - Automate network provisioning using Python, Ansible, Git, REST APIs, and Infrastructure-as-Code methodologies. - Monitor network health using UFM telemetry, Prometheus, Grafana, and other observability platforms. - Support high availability, maintenance windows, incident response, root cause analysis, and capacity planning. - Participate in architecture reviews and define networking standards for AI infrastructure. Required Qualifications - 7+ years of data center networking experience. - 3+ years supporting NVIDIA InfiniBand environments. - Hands-on experience with NVIDIA UFM Enterprise. - Experience deploying and operating Quantum and Quantum-2 InfiniBand switches. - Strong understanding of: - InfiniBand Architecture - Subnet Manager (SM) - Adaptive Routing - Congestion Control - Partition Keys (PKeys) - LIDs - Queue Pairs (QP) - Virtual Lanes (VL) - Service Levels (SL) - Strong Linux administration experience (Ubuntu). - Experience with automation using Python and Ansible. - Deep understanding of Layer 2 and Layer 3 networking. - Experience with BGP, EVPN, VXLAN, and modern spine-leaf architectures. - Experience with packet captures and troubleshooting using tcpdump, Wireshark, and ibdiagnet tools. - Excellent troubleshooting and communication skills. What You Bring - 10+ years of experience in large-scale data center networking - Deep expertise in spine-leaf architectures and L3 fabrics - Strong experience with BGP, EVPN, VXLAN - Experience operating high-performance computing (HPC) or GPU-dense environments - Experience designing networks for hyperscalers, neoclouds, or high-scale SaaS infrastructure - Strong automation background (Python, Ansible, Terraform, or similar) - Experience with network observability tooling and telemetry pipelines - Proven ability to design systems that scale to thousands of nodes - Strong documentation and architectural communication skills Nice to Have - Experience with Netris and Terraform. - Experience with multi-region backbone design - Exposure to bare-metal provisioning systems - Experience working in high-growth infrastructure startups Why This Role Matters The network is the foundation of distributed AI training. Performance bottlenecks at the fabric layer directly impact customer workloads. This role will shape how Voltage Park scales from tens of thousands of GPUs to significantly beyond — ensuring deterministic performance, predictable scaling, and enterprise-grade reliability. If you want to architect infrastructure that powers frontier AI research, this is the role. Compensation We are committed to offering competitive compensation that reflects the value each team member brings to our mission. Final offers are based on factors such as experience, skills, geographic location, and role expectations. In addition to base salary, the total rewards package for eligible roles includes a discretionary bonus, equity component, and comprehensive benefits. The anticipated annual base salary range for this role is: $170,000—$210,000 USD Benefits and Perks Benefits include: - Comprehensive medical, dental and vision coverage (U.S.); Private medical and dental insurance (U.K.) - Retirement and financial wellness support (U.S.); Pension contribution (U.K.) - Generous paid time off, plus holidays - Paid parental leave - Professional development support - Wellness and work-from-home stipends - Flexible work environment Lightning AI is committed to fostering an inclusive and diverse workplace and provides equal employment opportunities to all employees and applicants.

🏢 Summary: Senior Network Engineer role focused on designing, deploying, automating, and operating large-scale NVIDIA InfiniBand networking infrastructure for AI GPU clusters. The position involves managing high-performance AI networking fabrics, optimizing distributed AI workloads, and building scalable data center architectures using modern networking and automation technologies. The offer includes competitive compensation, equity, and comprehensive benefits. 🗂️ Requirements: 7+ years data center networking experience, 3+ years supporting NVIDIA InfiniBand environments, Hands-on experience with NVIDIA UFM Enterprise, Experience with Quantum and Quantum-2 InfiniBand switches, Knowledge of InfiniBand Architecture, Knowledge of Subnet Manager, Knowledge of Adaptive Routing, Knowledge of Congestion Control, Knowledge of PKeys, Knowledge of LIDs, Knowledge of Queue Pairs, Knowledge of Virtual Lanes, Knowledge of Service Levels, Linux administration experience, Python automation experience, Ansible automation experience, Layer 2 networking knowledge, Layer 3 networking knowledge, Experience with BGP, Experience with EVPN, Experience with VXLAN, Experience with spine-leaf architectures, Experience with tcpdump, Experience with Wireshark, Experience with ibdiagnet, Troubleshooting skills, Communication skills 📃 Skills: InfiniBand, UFM, NCCL, RoCEv2, Linux, Ubuntu, Python, Ansible, Git, REST, Prometheus, Grafana, BGP, EVPN, VXLAN, tcpdump, Wireshark, ibdiagnet, MPI, GPUDirect, RDMA, Terraform, Netris 🏢 Description: Who We Are Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction. Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in. We serve solo researchers, startups, and large enterprises. The Role We are seeking an experienced Senior Network Engineer (InfiniBand / UFM) to design, deploy, automate, and operate next-generation AI Factory networking infrastructure supporting large-scale GPU clusters. This role is responsible for building and maintaining high-performance NVIDIA Quantum InfiniBand fabrics that power AI training and inference environments utilizing NVIDIA UFM, NCCL, RoCEv2, and modern data center technologies. The ideal candidate has deep expertise in InfiniBand networking, NVIDIA Unified Fabric Manager (UFM), large-scale GPU deployments, Linux networking, automation, and troubleshooting distributed AI workloads. What You'll Do - Design, deploy, and maintain large-scale NVIDIA InfiniBand fabrics supporting AI/ML GPU clusters. - Deploy and administer NVIDIA Unified Fabric Manager (UFM) Enterprise for monitoring, provisioning, telemetry, and fabric health. - Configure and optimize NVIDIA Quantum and Quantum-2 InfiniBand switches. - Troubleshoot fabric performance issues impacting NCCL, MPI, GPUDirect RDMA, and AI training jobs. - Implement and validate fat-tree, Dragonfly+, Clos, and spine-leaf network architectures. - Perform firmware lifecycle management for InfiniBand switches, adapters (HCAs), and UFM infrastructure. - Optimize congestion control, adaptive routing, QoS, and traffic engineering for high-performance GPU communication. - Work closely with AI platform, GPU infrastructure, storage, and systems engineering teams to deploy scalable AI Factory environments. - Automate network provisioning using Python, Ansible, Git, REST APIs, and Infrastructure-as-Code methodologies. - Monitor network health using UFM telemetry, Prometheus, Grafana, and other observability platforms. - Support high availability, maintenance windows, incident response, root cause analysis, and capacity planning. - Participate in architecture reviews and define networking standards for AI infrastructure. Required Qualifications - 7+ years of data center networking experience. - 3+ years supporting NVIDIA InfiniBand environments. - Hands-on experience with NVIDIA UFM Enterprise. - Experience deploying and operating Quantum and Quantum-2 InfiniBand switches. - Strong understanding of: - InfiniBand Architecture - Subnet Manager (SM) - Adaptive Routing - Congestion Control - Partition Keys (PKeys) - LIDs - Queue Pairs (QP) - Virtual Lanes (VL) - Service Levels (SL) - Strong Linux administration experience (Ubuntu). - Experience with automation using Python and Ansible. - Deep understanding of Layer 2 and Layer 3 networking. - Experience with BGP, EVPN, VXLAN, and modern spine-leaf architectures. - Experience with packet captures and troubleshooting using tcpdump, Wireshark, and ibdiagnet tools. - Excellent troubleshooting and communication skills. What You Bring - 10+ years of experience in large-scale data center networking - Deep expertise in spine-leaf architectures and L3 fabrics - Strong experience with BGP, EVPN, VXLAN - Experience operating high-performance computing (HPC) or GPU-dense environments - Experience designing networks for hyperscalers, neoclouds, or high-scale SaaS infrastructure - Strong automation background (Python, Ansible, Terraform, or similar) - Experience with network observability tooling and telemetry pipelines - Proven ability to design systems that scale to thousands of nodes - Strong documentation and architectural communication skills Nice to Have - Experience with Netris and Terraform. - Experience with multi-region backbone design - Exposure to bare-metal provisioning systems - Experience working in high-growth infrastructure startups Why This Role Matters The network is the foundation of distributed AI training. Performance bottlenecks at the fabric layer directly impact customer workloads. This role will shape how Voltage Park scales from tens of thousands of GPUs to significantly beyond — ensuring deterministic performance, predictable scaling, and enterprise-grade reliability. If you want to architect infrastructure that powers frontier AI research, this is the role. Benefits and Perks Benefits include: - Comprehensive medical, dental and vision coverage (U.S.); Private medical and dental insurance (U.K.) - Retirement and financial wellness support (U.S.); Pension contribution (U.K.) - Generous paid time off, plus holidays - Paid parental leave - Professional development support - Wellness and work-from-home stipends - Flexible work environment At Lightning AI, we are committed to fostering an inclusive and diverse workplace. We believe that diverse teams drive innovation and create better products. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We are dedicated to building a culture where everyone can thrive and contribute to their fullest potential.

🏢 Summary: Senior .NET Engineer role focused on building enterprise-grade .NET applications and integrating advanced AI/ML solutions in production environments using Azure cloud technologies. The position involves AI model evaluation, prompt engineering, cloud architecture, and scalable microservices development while collaborating with technical and business stakeholders. 🗂️ Requirements: Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, or related field, 5+ years of professional software development experience in .NET/C# ecosystem, Hands-on experience integrating AI technologies in commercial projects, Strong understanding of AI/ML concepts, model training, and data preprocessing, Expertise in prompt engineering, RAG architectures, and vector databases, Experience selecting, fine-tuning, and implementing AI models and frameworks, Experience with Microsoft Azure AI and cloud services, Knowledge of SOLID principles, design patterns, and microservices architecture, Experience with CI/CD pipelines and SQL/NoSQL databases, Ability to explain AI concepts to technical and non-technical stakeholders, Strong analytical and debugging skills, Experience working in agile environments and mentoring engineers 📃 Skills: C#, .NET, ASP.NET, Microservices, AI, ML, LLM, NLP, Azure, AzureOpenAI, AzureML, AzureFunctions, CosmosDB, AKS, RAG, SQL, NoSQL, CI/CD, PromptEngineering 🏢 Description: We are seeking a highly skilled and forward-thinking Senior .NET Engineer with AI expertise to join our growing engineering team. In this role, you will bridge the gap between traditional enterprise .NET development and cutting-edge Artificial Intelligence. Responsibilities - Architect, develop, and maintain high-performance, scalable, and secure enterprise-grade applications using modern .NET stack (C#, .NET 8, ASP.NET Core, Microservices) - Lead the hands-on integration and leveraging of AI technologies in real-world, production-ready projects - Evaluate, select, and implement optimal AI models, frameworks, and orchestrators beyond basic API/tool usage to solve complex business problems - Apply a strong understanding of AI/ML concepts to drive high-quality model training, dataset preprocessing, and advanced prompt engineering techniques to maximize output accuracy and relevance - Proactively identify high-value opportunities within current workflows and products to apply AI for process automation, advanced analytics, or next-generation product enhancements - Design and manage cloud infrastructure utilizing Microsoft Azure technologies including Azure OpenAI Service, Azure Cognitive Services, Azure Machine Learning, Azure Functions, Cosmos DB, and AKS to deploy and scale AI workloads - Explain complex AI concepts, workflows, architectural designs, and trade-offs clearly to both technical developers and non-technical business stakeholders - Establish and adhere to guidelines regarding security, rate limits, latency optimization, data privacy, and ethical considerations during model deployment Requirements - Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, or a related field (or equivalent practical experience) - 5+ years of professional software development experience with emphasis on the .NET/C# ecosystem - Demonstrated hands-on experience integrating and leveraging AI technologies such as LLMs, NLP, Computer Vision, or recommendation engines in commercial projects - Strong understanding of AI/ML concepts including model training, data preprocessing, and optimization - Expertise in advanced prompt engineering, RAG architectures, and vector databases - Ability to analyze, fine-tune, select, and implement AI models and frameworks based on performance, cost, and latency constraints - Proven experience applying AI for process automation, analytics, or product enhancement - Familiarity with AI limitations and best practices in adoption and deployment - Proven experience working with Microsoft Azure technologies, specifically deploying AI workloads through Azure OpenAI, Azure ML pipelines, and standard cloud services - Solid understanding of SOLID principles, design patterns, microservices architecture, CI/CD pipelines, and database management (SQL and NoSQL) - Strong capacity to explain AI concepts, workflows, and system designs to diverse audiences - Strong analytical and debugging skills with a proactive mindset - Comfortable working in an agile environment, mentoring junior engineers, and collaborating with Product Owners We offer - Engineering community of industry professionals - Friendly team and enjoyable working environment - Flexible schedule and remote work opportunity within Poland - Opportunity to work abroad for up to 60 days annually - Business-driven relocation opportunities - Career roadmap and leadership development programs - Certification opportunities for GCP, Azure, and AWS - Unlimited access to LinkedIn Learning, Get Abstract, and Cloud Guru - English classes - Stable income with Employment Contract or B2B options - Employee Stock Purchase Plan participation - Benefits package including health insurance, multisport, and shopping vouchers - Modern office spaces with entertainment and relaxation zones - Referral bonuses - Corporate, social, and well-being events Please note: The set of bonuses might vary based on the role.

🏢 Summary: The role focuses on designing, delivering, and operating a global AWS network backbone across multiple regions, with emphasis on Cloud WAN, centralized egress architectures, and automation-first infrastructure. It involves building secure, scalable, and cost-efficient network solutions using Infrastructure as Code and driving network reliability through testing, monitoring, and continuous improvement. The position also includes hands-on technical leadership and implementation of network automation in complex multi-region environments. 🗂️ Requirements: 5+ years of network engineering experience in enterprise cloud environments, Deep hands-on expertise with AWS Cloud WAN and multi-region connectivity, Strong proficiency in Terraform for infrastructure as code, Experience designing centralized egress architectures with AWS Network Firewall, Experience delivering large-scale network infrastructure projects, Experience with network automation and DevOps practices, Ability to troubleshoot complex multi-region network issues, Experience with AI-powered coding assistants in software delivery, Work permit and residence within the European Union, Fluent English 📃 Skills: AWS, CloudWAN, Terraform, NetworkFirewall, VPC, Routing, Networking, Automation, DevOps, IaC, Git, CI/CD, Monitoring, Firewall, AI 🏢 Description: 🟣 You will be: delivering the entire global network backbone infrastructure across multiple AWS regions, building centralised egress architectures using AWS Network Firewall to ensure secure, scalable, and cost-effective traffic management across all regions, building and maintaining AWS Cloud WAN infrastructure to provide seamless connectivity and routing across the global network footprint, developing and maintaining infrastructure as code using Terraform to ensure consistent, repeatable, and version-controlled network deployments, collaborating with security architects, cloud engineers, and other stakeholders to gather requirements and translate them into robust network architectures and technical specifications, driving the implementation of network automation and self-service capabilities to enable parallel development and incremental delivery of network services, leading by example through hands-on technical work while mentoring other network engineers and providing technical guidance and knowledge sharing, ensuring the reliability, performance, and security of network infrastructure through comprehensive testing, monitoring, and continuous improvement, driving the adoption of best practices in network engineering, including infrastructure as code and automation-first approaches, troubleshooting and resolving complex network issues across multi-region environments on time, staying up to date with the latest AWS networking services, industry trends, and technologies to ensure the infrastructure remains competitive and efficient. 🟣 Your profile: 5+ years of experience in network engineering with a strong focus on enterprise cloud networking solutions, practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery, deep hands-on expertise in AWS Cloud WAN, including configuration, routing policies, and multi-region connectivity, strong proficiency in Terraform and proven experience managing complex network infrastructure deployments, extensive experience designing and implementing centralised egress architectures using AWS Network Firewall, including policy management and traffic inspection, proven track record of delivering large-scale network infrastructure projects in fast-paced environments, excellent problem-solving skills with the ability to think critically and creatively about complex network challenges, strong communication and collaboration skills with the ability to work effectively in cross-functional and team-oriented environments, experience working with agile methodologies and DevOps practices applied to network infrastructure, ability to use a data-driven approach to network optimisation, capacity planning, and decision-making, fluent English communication skills. 🟣 Nice to have: experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work, interest in and familiarity with emerging AI-driven practices (e.g. agent-based workflows, automation patterns, AI-augmented development), with a willingness to explore and experiment beyond standard approaches. Work from the European Union region and a work permit are required. 🟣 Recruitment Process: CV review – HR Call – Interview – Client Interview – Decision 🎁 Benefits 🎁 ✍ Development: development budget of up to 6,800 PLN, we fund certifications e.g.: AWS, Azure, ISTQB, PSM, access to Udemy, Safari Books Online and more, events and technology conferences, technology Guilds, internal training, Xebia Library, Xebia Upskill. 🩺 We take care of your health: private medical healthcare, MultiSport card - we subsidise a MultiSport card, mental Health Support. 🤸‍♂️ We are flexible: flexible working hours, B2B or permanent contract, contract for an indefinite period.

🏢 Summary: High-impact compute infrastructure role focused on building and optimizing one of the world’s largest AI supercomputers for large-scale training and inference workloads. The position involves low-level GPU optimization, Linux kernel and virtualization work, distributed systems, and orchestration across massive GPU clusters. Engineers will collaborate closely with AI research teams to maximize performance, scalability, and reliability. 🗂️ Requirements: Deep systems programming experience in C/C++ or Rust, Experience with large-scale GPU clusters or distributed compute infrastructure, Hands-on GPU kernel optimization experience, Knowledge of Linux kernel internals, scheduling, virtualization, or orchestration, Experience building high-performance AI infrastructure for training or inference, Ability to optimize memory-bound and compute-bound workloads, Experience with exabyte-scale storage systems 📃 Skills: CUDA, CUTLASS, Tensor, Nsight, Linux, KVM, Firecracker, Kubernetes, C++, Rust, GPU, CUDA, GeMM, Attention 🏢 Description: SpaceXAI's mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company's mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates.ABOUT THE ROLE: We are building one of the world's largest AI supercomputers from the ground up. As part of the Compute Infrastructure team, you will own both the raw GPU supercomputer and the platform layer that runs on top of it. You will work across the full stack — from low-level GPU kernel optimizations and Linux kernel internals to massive-scale orchestration and virtualization — to make training and inference at SpaceXAI as fast, reliable, and scalable as possible. This is a broad, high-impact role that combines hardcore supercompute and compute infrastructure work. Your contributions will directly accelerate Grok's training speed and overall AI progress. RESPONSIBILITIES: Design, build, and optimize massive GPU clusters for extreme-scale training and inference workloads Develop and tune low-level CUDA kernels (GeMM, Attention, etc.), using CUTLASS, Tensor Cores, and Nsight for maximum performance Profile, debug, and eliminate bottlenecks across GPU memory hierarchy, networking fabric, filesystems, and multi-GPU operation Collaborate closely with AI research teams to deliver production-grade performance and scalability PREFERRED SKILLS AND EXPERIENCE: Deep low-level systems programming (C/C++/PTX/SASS) Strong experience with large-scale GPU clusters or distributed compute infrastructure at production scale Hands-on work with GPU kernel optimization (CUTLASS, custom kernels, Nsight profiling) Track record of building or running high-performance infrastructure for AI workloads (training or inference platforms) Ability to reason from first principles and optimize for both memory-bound and compute-bound scenarios COMPENSATION AND BENEFITS: $180,000 - $440,000 USD Base salary is just one part of our total rewards package at SpaceXAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks.SpaceXAI is an equal opportunity employer. For details on data processing, view our Recruitment Privacy Notice.