Sr. Site Reliability Engineer (SRE)

🏢 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: 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 role focuses on designing and building a high-performance Software-Defined Networking (SDN) platform for AI and research workloads, enabling scalable, isolated, and automated networking using DPU-accelerated infrastructure. You will develop orchestration, provisioning, and lifecycle management systems integrating Kubernetes, SLURM, and GPU clusters. The position emphasizes programmable networking, multi-tenancy, and low-latency connectivity for distributed computing environments. 🗂️ Requirements: 5+ years of software engineering experience in network platforms or SDN systems, Strong knowledge of Kubernetes networking architecture and CNI plugins, Deep understanding of TCP/IP, VLANs, VXLAN, BGP, OSPF, and data center networking, Proven experience with SDN concepts and network virtualization, Proficiency in Go and Python, Strong experience with Linux networking stack and Open vSwitch, Familiarity with DPU or SmartNIC architectures and SR-IOV, Understanding of RDMA, RoCE, InfiniBand, and low-latency networking, Experience designing scalable network orchestration or automation systems 📃 Skills: Go, Python, Kubernetes, CNI, TCP/IP, VLAN, VXLAN, BGP, OSPF, SDN, Linux, OpenvSwitch, SR-IOV, Bluefield, RDMA, RoCE, InfiniBand, Terraform, FastAPI, gRPC 🏢 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 our software-defined networking (SDN) platform that enables high-performance, isolated networking for distributed computing, model training, inference, and data-intensive workloads. Working closely with our network, infrastructure, and product teams, you'll design and implement the network orchestration and provisioning systems that manage DPU-accelerated networking, tenant isolation, and network lifecycle management – enabling researchers and engineers to access enterprise-grade networking with cloud-like simplicity. Job Responsibilities: Software-Defined Networking Architecture: Collaborate with infrastructure to design and build scalable SDN orchestration systems leveraging NVIDIA Bluefield-3 DPUs to deliver programmable, high-performance networking for AI workloads with hardware-accelerated forwarding isolation. Research Cluster Networking: Design and implement networking systems for research computing environments including Kubernetes and SLURM clusters, enabling high-performance connectivity, optimized network topology for distributed workloads, and seamless integration with cluster orchestration systems. Network Provisioning & Lifecycle Management: Implement automated SDN provisioning systems that handle VPC creation, subnet allocation, routing configuration, and network resource lifecycle from deployment through decommissioning. DPU Platform Engineering: Develop platform capabilities for managing Bluefield-3 DPUs including SR-IOV virtual function management, OVS offload configuration, network function deployment, and integration with compute orchestration systems. Multi-Tenancy & Network Isolation: Build enterprise-grade network isolation using VPCs, VXLAN, and hardware-accelerated forwarding to ensure complete tenant separation while maintaining high-performance connectivity for GPU clusters and distributed workloads. High-Performance Networking: Collaborate with infrastructure to optimize network paths for RDMA, RoCE, and GPU-to-GPU communication, ensuring minimal latency and maximum throughput for distributed training and large-scale computational workloads. Network APIs & Integration: Develop robust APIs and SDKs for network resource management that integrate seamlessly with compute and storage platforms, enabling programmatic network provisioning and configuration. Network Observability: Implement comprehensive network monitoring, telemetry, and troubleshooting systems that provide visibility into network performance, utilization, and tenant traffic patterns.Security & Policy Management: Build platform network security features including security groups, firewall rules, and policy enforcement that protect tenant workloads while enabling flexible network configuration. Requirements: Experience: 5+ years in software engineering with proven experience building network platforms, SDN systems, or network automation for production environments. Kubernetes Networking & Container Orchestration: Strong familiarity with Kubernetes networking architecture, CNI plugins, service networking, and network policies. Understanding of pod networking, services, ingress, and how Kubernetes manages network resources. Networking Expertise: Deep understanding of networking fundamentals including TCP/IP, VLANs, VXLAN, BGP, OSPF, routing protocols, and data center network architectures.Software-Defined Networking: Background in SDN concepts, network virtualization, overlay networks, and programmable networking technologies. Programming Skills: Experience with Go and Python for performance-critical networking components and services is highly valued. Linux Networking: Strong experience with Linux networking stack, including network namespaces, iptables/nftables, Open vSwitch, and kernel networking systems. DPU & SmartNIC Experience: Familiarity with DPU/SmartNIC architectures (Bluefield, or similar), SR-IOV, hardware offload capabilities, and programmable networking hardware – or strong ability to learn quickly. High-Performance Networking: Understanding of RDMA, RoCE, Infiniband, and low-latency networking requirements for distributed computing and GPU workloads. Problem-Solving & Architecture: Demonstrated ability to solve complex networking performance and scalability challenges while balancing pragmatic shipping with good long-term architecture. Autonomy & Communication: Comfortable navigating ambiguity, defining requirements collaboratively, and communicating technical decisions through clear documentation. Commitment to Growth: Growth mindset with continuous focus on learning and professional development. Preferred Qualifications Background provisioning or managing networking for research computing environments (Kubernetes, SLURM, or HPC clusters) Experience with NVIDIA Bluefield DPU programming and DOCA framework Background with network function virtualization (NFV) and service function chaining Knowledge of Kubernetes networking (CNI plugins, network policies, service mesh) Experience building network control planes or SDN controllers Familiarity with network automation frameworks and infrastructure-as-code for networking Understanding of data center fabric architectures (spine-leaf, CLOS topologies) Experience with network security and compliance requirements in regulated industries Background building networking for research institutions, HPC environments, or cloud providers Key Technologies Go, Python, NVIDIA Bluefield DPUs, Open vSwitch, VXLAN, SR-IOV, RDMA, RoCE, InfiniBand, BGP, Linux networking, Terraform, FastAPI, gRPC 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: Hands-on DevOps Engineer role focused on building and operating automation, deployment, and reliability standards for large-scale GPU infrastructure supporting AI training and inference. The position involves Infrastructure as Code, CI/CD, observability, security, and low-level automation across bare-metal servers, networking, storage, and Kubernetes-based platforms. The role emphasizes reliability, scalability, and automation in complex, high-performance environments. 🗂️ Requirements: 4–7 years in DevOps, SRE, or Platform Engineering, Experience with infrastructure automation in production environments, Hands-on experience with Terraform or Ansible, Experience building and maintaining CI/CD pipelines, Knowledge of GitOps practices, Understanding of infrastructure security and vulnerability management, Experience with security tools (e.g., Snyk, CrowdStrike), Practical experience with Kubernetes, Experience with GPU technologies (e.g., NVIDIA GPU Operator, MIG), Scripting or programming skills in Python, Go, or Bash, Experience with bare-metal provisioning or low-level infrastructure automation, Knowledge of observability tools (Prometheus, Grafana, Loki, OpenTelemetry) 📃 Skills: Terraform, Ansible, Kubernetes, Python, Go, Bash, Prometheus, Grafana, Loki, OpenTelemetry, Snyk, CrowdStrike, NVIDIA, MIG, CI/CD, GitOps 🏢 Description: About the role We are looking for a DevOps Engineer to help build and operate automation, deployment, and reliability standards for large-scale GPU infrastructure used for AI training and inference workloads. In this role, you will work on software-defined infrastructure supporting GPU clusters, high-performance networking, storage platforms, and internal AI services. This is a hands-on position for someone who is comfortable working close to infrastructure, improving operational processes, and building reliable automation in a complex technical environment. Responsibilities Design, implement, and maintain Infrastructure as Code solutions for provisioning and managing bare-metal GPU servers, networking, storage, and cluster orchestration components Build and improve CI/CD pipelines for infrastructure, platform services, and internal tooling Develop and maintain monitoring, logging, alerting, and observability solutions for large-scale GPU environments Support reliability initiatives by defining and tracking SLIs/SLOs , automating incident response, and contributing to post-incident analysis Automate operational tasks such as cluster scaling, firmware and BIOS updates, hardware validation, diagnostics, and capacity planning Work closely with Infrastructure, Networking, Facilities, and AI/ML teams to ensure stable and scalable platform operations Support DevSecOps practices, including infrastructure hardening, vulnerability management, and compliance automation Identify repetitive manual work and replace it with efficient automation Evaluate new tools and solutions related to GPU infrastructure, orchestration, and cloud-native operations Requirements 4–7 years of experience in DevOps, SRE, Platform Engineering , or a similar role Strong practical experience with infrastructure automation in complex production environments Good hands-on knowledge of Terraform, Ansible , or similar Infrastructure as Code tools Experience building and maintaining CI/CD pipelines and working with GitOps practices Good understanding of infrastructure security, vulnerability management, and security best practices Experience with security tools such as Snyk, CrowdStrike , or similar solutions Practical experience with Kubernetes Experience working with GPU-related technologies such as NVIDIA GPU Operator, device plugins, MIG, or time-slicing Good scripting or programming skills in Python, Go, or Bash Experience with bare-metal provisioning, low-level infrastructure automation, or data center operations Good knowledge of observability tools such as Prometheus, Grafana, Loki, and OpenTelemetry Ability to work independently, prioritize tasks, and communicate effectively with technical teams English proficiency at least at a communicative level is required, as you will be working in an international team Nice to have Experience in AI infrastructure, HPC environments, hyperscale infrastructure, or data center operations Familiarity with orchestration and scheduling tools such as Slurm, Ray, Run:ai, KServe , or Kubernetes-based schedulers Experience integrating telemetry from power, cooling, or environmental systems Experience building internal platforms or self-service tools for engineering teams Understanding of compliance and audit requirements in security-sensitive environments What we offer Benefits package Opportunity to work on advanced infrastructure supporting large-scale AI workloads Real impact on the reliability and scalability of next-generation compute environments Collaboration with experienced engineers across infrastructure, platform, and AI domains A fast-moving environment with space for ownership, technical input, and professional growth