Senior Fullstack Engineer

🏢 Summary: Lead a hands-on full-stack/backend engineering effort to build scalable services, APIs, workflow engines, and agentic AI systems for logistics optimization. The role combines technical leadership, production reliability, third-party integrations, and mentoring while delivering AI-driven planning and optimization capabilities. 🗂️ Requirements: Senior or technical-lead backend engineering experience, Strong Python and API-development proficiency, Scalable distributed-systems architecture experience, API design, data modeling, and integration expertise, Agentic AI, LLM orchestration, RAG, or multi-agent systems experience, Cloud-platform and CI/CD experience, Third-party API integration experience, Production debugging experience with LLM-driven systems, Engineering mentorship and code/design review experience, Technical leadership and stakeholder communication skills, Successful pre-employment screening 📃 Skills: Python, FastAPI, TypeScript, LangGraph, LangChain, RAG, AWS, Bedrock, GitLab, CICD, SSE, WebSockets, LLM 🏢 Description: What's the role? We are looking for a Tech Lead Fullstack Engineer to build core backend systems powering next-generation agentic AI solutions for the logistics industry. The systems developed by this team optimize deliveries at scale improving driver efficiency, increasing reliability, reducing operational costs, and lowering environmental impact. In this role, you will focus on building the core service layer, customer-facing APIs, workflow engines, and integrations with HERE APIs and third-party systems that enable intelligent logistics workflows. You will work closely with senior engineers and technical leaders to design and implement scalable, reliable systems that support AI-driven planning and optimization use cases. This is a hands-on engineering role with a strong focus on backend development, system reliability, and production-quality delivery . What You’ll Do: Lead the design, build, and maintenance of core backend services, APIs, and workflow orchestration systems for logistics applications Drive integrations with HERE APIs and external third-party systems to support real-world logistics workflows Translate ambiguous product and technical requirements into scalable, maintainable system components, and help others on the team do the same Write clean, efficient, and well-tested code in Python and/or TypeScript, setting the standard for code quality across the team Design and build agentic AI systems based on LangGraph, and help shape how the team approaches LLM orchestration and multi-agent architecture Lead system design discussions and own key architectural decisions, ensuring solutions align with the overall system vision Build and maintain customer-facing APIs with a strong focus on usability, performance, and reliability Ensure high-quality delivery through testing, monitoring, and debugging in production environments, including systems with non-deterministic (LLM-driven) behavior Mentor engineers through code review, design feedback, and pairing, and help grow the team's technical capabilities Collaborate with engineers, product managers, and domain experts to deliver end-to-end features Continuously improve system performance, scalability, and operational efficiency Leverage AI-assisted development tools to improve development speed and code quality Who are you? What We’re Looking For: Proven experience as a technical lead or in a senior engineering role, with strong backend development skills and a track record of owning systems end-to-end Strong proficiency in Python, with experience building APIs (FastAPI or similar) and backend systems; TypeScript is a plus Experience architecting scalable services and distributed systems, and making tradeoff decisions that hold up under real-world load and change Strong command of API design, data modeling, and system integration patterns — ideally including streaming/event-driven APIs (e.g., SSE, websockets), not just request/response Hands-on experience building or leading development of agentic AI systems: LLM orchestration frameworks (e.g., LangGraph, LangChain), RAG pipelines, or multi-agent architectures Experience working with cloud platforms, ideally AWS (including managed LLM services like Bedrock), and modern CI/CD practices (GitLab CI or equivalent) Experience integrating with external/third-party APIs and handling real-world data and system constraints in production Strong debugging and problem-solving skills in production environments, including systems with non-deterministic (LLM-driven) components Experience mentoring engineers, conducting code/design reviews, and raising the technical bar across a team Ability to break down ambiguous problems into well-defined work, balancing hands-on coding with technical leadership Excellent communication skills, with the ability to align engineering decisions with product and business priorities, and a pragmatic approach to engineering challengesWe are looking for a Lead Software Engineer to build core backend systems powering next-generation agentic AI solutions for the logistics industry. The systems developed by this team optimize deliveries at scale improving driver efficiency, increasing reliability, reducing operational costs, and lowering environmental impact.Design , build, and maintain What We Offer: The opportunity to tackle meaningful and challenging problems A chance to continuously learn and stay on top of the latest technology trends Work that has real-world impact, shaping the future of mobility and technology Regular feedback to help you grow and succeed in your role A collaborative and supportive team environment where your contributions are valued Competitive salary plus bonus Flexible working hours and a hybrid working environment Medical coverage for you and your family This role is eligible for Creative Tax Incentive scheme in Poland” or KUP (Autorskie Koszty Uzyskania Przychodu) Option to work on a B2B contract (please note: benefits, bonus and KUP do not apply in this case) Change is HERE. Apply Now! #LI-AK8   #LI-HYBRID Life at HERE in Poland comes with a competitive total rewards package designed to support your health, wellbeing, and performance. This includes a base salary, a Short-Term Incentive (STI) bonus (percentage based on role), a creative tax advantage for eligible positions, private medical care (including dental), life insurance, a meal allowance, vision reimbursement, a remote work allowance (if applicable), access to MyBenefit and Multisport programs, and various wellbeing initiatives. Paid time off, sick leave, and parental leave are provided in accordance with the Polish Labor Code. As part of HERE Technologies employment process, candidates will be required to successfully complete a pre-employment screening process. This offer and any related claims are subject to the successful completion of a pre-employment screening. This will involve employment, education, and criminal verification if applicable. HERE is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, age, gender identity, sexual orientation, marital status, parental status, religion, sex, national origin, disability, veteran status, and other legally protected characteristics. Who are we? HERE Technologies is a location data and technology platform company. We empower our customers to achieve better outcomes – from helping a city manage its infrastructure or a business optimize its assets to guiding drivers to their destination safely. At HERE we take it upon ourselves to be the change we wish to see. We create solutions that fuel innovation, provide opportunity and foster inclusion to improve people’s lives. If you are inspired by an open world and driven to create positive change, join us. Learn more about us on our YouTube Channel. Apply for this job online Email this job to a friend Share on your newsfeed Connect With Us! Not ready to apply? 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🏢 Summary: Backend Engineer role focused on building and operating the AI inference and orchestration systems behind a production AI assistant. The position emphasizes low-latency, high-throughput APIs, distributed-system reliability, observability and performance optimization for mobile, desktop and machine-learning integrations. Remote-first collaboration is available for a candidate working from Poland, with base salary and equity assessed individually. 🗂️ Requirements: Production backend engineering experience, High-throughput, low-latency service experience, AI inference familiarity: LLMs, embeddings, multimodal models, Distributed-systems debugging under load, Production observability and incident-management knowledge, Performance-optimization expertise, Independent delivery in ambiguous environments, Working from Poland, Working-hours overlap with immediate team 📃 Skills: Python, Node.js, PyTorch, OpenAI, Anthropic, LLMs, Embeddings, SQL, NoSQL, Kubernetes, Docker, APIs, Caching, Batching, Streaming, Logging, Alerting 🏢 Description: About the product The product is an AI-native assistant designed to support everyday communication, organization, errands, and complex workflows with minimal user input. It must reliably manage long-running processes, retain context, interact with external tools, and complete real-world tasks despite the non-deterministic nature of modern AI models. Its objective is to make everyday activities significantly faster and easier for users. About the role As a Backend Engineer specializing in AI systems, you will own the inference and orchestration layer behind the product’s AI interactions. Your work will sit between models and end users, where latency, correctness, reliability, and operating costs directly affect the product experience. You will build and operate production systems that transform model capabilities into fast, stable, and observable APIs used by mobile and desktop applications. What you will do • Build and operate backend systems serving AI-powered functionality in production. • Design inference pipelines, orchestration layers, and service boundaries around Machine Learning models. • Take ownership of production monitoring, logging, alerting, and incident response. • Optimize latency and throughput across inference, caching, batching, and streaming. • Build reliable APIs supporting integration with mobile, frontend, and Machine Learning systems. • Diagnose distributed-system issues under production load. • Continuously improve system performance and reliability using real-world production signals. What we are looking for • Strong backend engineering fundamentals supported by experience with production systems. • Experience building or operating high-throughput, low-latency services. • Familiarity with AI inference patterns, including LLMs, embeddings, and multimodal models. • Confidence debugging distributed systems under load. • Understanding of production observability, incident management, and performance optimization. • A delivery-oriented mindset focused on shipping, observing production behavior, and improving through iteration. • Ability to work independently and make pragmatic engineering decisions in an ambiguous environment. What success looks like • Backend systems reliably handle production AI traffic at scale with low latency and high throughput. • APIs remain stable, clear, and easy to integrate with frontend, mobile, and Machine Learning systems. • Production incidents are detected quickly, diagnosed effectively, and resolved with minimal impact on users. • System performance and reliability improve continuously based on real-world usage and production data. Technology stack • Python • Node.js • PyTorch • OpenAI, Anthropic, and open-source LLMs • SQL and NoSQL databases • Kubernetes • Docker Compensation Compensation is assessed individually based on professional experience and technical capability, scope of responsibility, location and relevant market benchmarks, and expected impact on the product and organization. Candidates may share their expected compensation at the beginning of the recruitment process. The compensation package consists of a base salary and equity, with flexibility for exceptional candidates. A company laptop will be provided where required for the role. Remote work and global collaboration The organization operates as a remote-first, globally distributed team. There is no fixed company-wide working schedule or requirement to follow one specific time zone. Team members are expected to maintain sufficient working-hours overlap with their immediate colleagues to collaborate effectively. The successful candidate will work from Poland and collaborate with backend, Machine Learning, mobile, and product specialists across different regions. Poland-based employees join existing global teams rather than a separate local team. The exact reporting line and hiring manager will be confirmed during the recruitment process. How the team works Products are built by small, highly capable, hands-on teams. Decisions are made collaboratively, while individuals are expected to take ownership, bring structure to ambiguous problems, and execute independently. The team moves quickly while balancing production quality, experimentation, and continuous learning from real user behavior. There is no fixed hiring quota for this position. The focus is on identifying engineers who meet the required technical and ownership standards rather than filling a predetermined number of seats. Recruitment process The standard recruitment process consists of up to four stages: • Technical assessment, where relevant to the candidate’s background. • HR interview. • One or more technical interviews. • Founder or leadership interview. Particularly strong candidates may be fast-tracked directly to the technical interview stage based on their experience and previous work. Applications are evaluated by members of the technical team. Interviews may be conducted virtually and, where relevant, onsite. The exact format and interviewers may vary. Decisions are made efficiently, with candidates provided a prompt outcome.

🏢 Summary: Full Stack Engineer role focused on building an AI-native assistant that delivers reliable, persistent, goal-oriented workflows. The position covers frontend, backend and AI integrations, including agent planning, tool use, LLM reliability and real-time interactions in a remote-first global team. 🗂️ Requirements: Professional full-stack engineering experience across frontend and backend systems, System design, distributed applications and API architecture knowledge, Experience with LLMs, RAG systems or AI-powered applications, Ability to own features from concept through production deployment, Pragmatic decision-making in ambiguous, fast-moving environments, Cross-functional collaboration with engineering, Machine Learning and product teams 📃 Skills: Next.js, Python, Node.js, PyTorch, OpenAI, Anthropic, LLMs, RAG, SQL, NoSQL, Kubernetes, Docker, APIs 🏢 Description: About the role This role focuses on building the product layer that turns advanced AI capabilities into useful, production-grade workflows. The engineer will design how AI agents plan and operate, interact with external tools, manage failures, recover from errors and deliver consistent value to users. This is an end-to-end engineering role covering frontend development, backend systems and AI integrations. What you will do - Build end-to-end product functionality across frontend, backend and AI integrations. - Design agent workflows covering planning, tool use, multi-step execution, failure handling and recovery. - Integrate LLMs, memory systems and external tools into applications that operate reliably under real-world conditions. - Design real-time AI interactions involving streaming, partial results and strict latency requirements. - Improve system reliability, observability and fallback mechanisms. - Collaborate closely with Machine Learning, backend and product teams to deliver functionality from concept to production. - Continuously improve the product based on real-world usage, evaluation results and observed failure modes. What we are looking for - Strong professional experience in full-stack engineering across frontend and backend systems. - Solid understanding of system design, distributed applications and API architecture. - Experience working with LLMs, RAG systems or other AI-powered applications. - Ability to operate effectively in ambiguous situations and make pragmatic engineering decisions. - Strong ownership and the ability to take functionality from an initial idea through implementation and production deployment. - Confidence working in a fast-moving environment with evolving product requirements. - Ability to collaborate effectively across engineering, Machine Learning and product teams. What success looks like - AI-native product features progress beyond basic chat interfaces into persistent, goal-oriented workflows. - Agent workflows reliably complete multi-step tasks across external tools and multiple user sessions. - AI interactions remain responsive and achieve low latency without compromising output quality. - Production systems include robust fallback and recovery mechanisms for LLM and external-tool failures. - The reliability and completion rate of AI workflows improve through continuous evaluation, monitoring and iteration. - Reusable patterns and abstractions support scalable integration of LLMs, memory and external tools. - The resulting product experience feels proactive, consistent and dependable to users. Technology stack - Next.js - Python - Node.js - PyTorch - OpenAI, Anthropic and open-source LLMs - SQL and NoSQL databases - Kubernetes - Docker Compensation and employment The position is offered under an employment contract. Compensation is assessed individually based on professional experience and technical capability, scope of responsibility, location and relevant market benchmarks, and expected impact on the product and organization. Candidates may share their expected compensation at the beginning of the recruitment process. The compensation package consists of a base salary and equity, with flexibility for exceptional candidates. A company laptop will be provided where required for the role. Remote work and global collaboration The organization operates remotely with globally distributed teams. There is no fixed company-wide working schedule or required time zone; team members must maintain sufficient overlap with immediate colleagues for effective collaboration. The successful candidate will work from Poland and collaborate with frontend, backend, Machine Learning and product specialists across different regions. Poland-based employees join existing global teams. The reporting line and hiring manager will be confirmed during the recruitment process. How the team works The role is part of a small, hands-on team where decisions are made collaboratively and individuals are expected to take ownership, bring structure to ambiguous problems and execute independently. The team balances production quality, experimentation and continuous learning from real user behavior. Recruitment process The standard process includes up to four stages: - Technical assessment, where relevant to the candidate’s background. - HR interview. - One or more technical interviews. - Founder or leadership interview. Particularly strong candidates may be fast-tracked directly to the technical interview stage based on experience and previous work. Applications are evaluated by members of the technical team. Interviews may be virtual and, where relevant, onsite; the exact format and interviewers may vary.

🏢 Summary: Full Stack Engineer role focused on building an AI-native assistant that delivers reliable, persistent, goal-oriented workflows. The position covers frontend, backend and AI integrations, including agent planning, external-tool use, LLM reliability and real-time interactions in a remote-first global team. 🗂️ Requirements: Professional full-stack engineering experience, Frontend and backend systems experience, System design expertise, Distributed applications knowledge, API architecture knowledge, LLM, RAG or AI-powered application experience, Production deployment ownership, Cross-functional collaboration with Engineering, Machine Learning and Product teams 📃 Skills: Next.js, Python, Node.js, PyTorch, OpenAI, Anthropic, LLMs, RAG, SQL, NoSQL, Kubernetes, Docker, APIs 🏢 Description: About the product The product is an AI-native assistant designed to support everyday communication, organization, errands and complex workflows with minimal user input. It must reliably manage long-running processes, retain context, interact with external tools and complete real-world tasks despite the non-deterministic nature of modern AI models. The objective is to make everyday activities significantly faster and easier for users. About the role We are looking for a Full Stack Engineer specializing in AI systems to build the product layer that transforms advanced AI capabilities into useful, production-grade workflows. You will help design how AI agents plan and operate, interact with external tools, manage failures, recover from errors and deliver consistent value to users. This is an end-to-end engineering role covering frontend development, backend systems and AI integrations. What you will do - Build end-to-end product functionality across frontend, backend and AI integrations. - Design agent workflows covering planning, tool use, multi-step execution, failure handling and recovery. - Integrate LLMs, memory systems and external tools into applications that operate reliably under real-world conditions. - Design real-time AI interactions involving streaming, partial results and strict latency requirements. - Improve system reliability, observability and fallback mechanisms. - Collaborate closely with Machine Learning, backend and product teams to deliver functionality from concept to production. - Continuously improve the product based on real-world usage, evaluation results and observed failure modes. What we are looking for - Strong professional experience in full-stack engineering across frontend and backend systems. - Solid understanding of system design, distributed applications and API architecture. - Experience working with LLMs, RAG systems or other AI-powered applications. - Ability to operate effectively in ambiguous situations and make pragmatic engineering decisions. - Strong ownership and the ability to take functionality from an initial idea through implementation and production deployment. - Confidence working in a fast-moving environment with evolving product requirements. - Ability to collaborate effectively across engineering, Machine Learning and product teams. What success looks like - AI-native product features progress beyond basic chat interfaces into persistent, goal-oriented workflows. - Agent workflows reliably complete multi-step tasks across external tools and multiple user sessions. - AI interactions remain responsive and achieve low latency without compromising output quality. - Production systems include robust fallback and recovery mechanisms for LLM and external-tool failures. - The reliability and completion rate of AI workflows improve through continuous evaluation, monitoring and iteration. - Reusable patterns and abstractions support scalable integration of LLMs, memory and external tools. - The resulting product experience feels proactive, consistent and dependable to users. Technology stack - Next.js - Python - Node.js - PyTorch - OpenAI, Anthropic and open-source LLMs - SQL and NoSQL databases - Kubernetes - Docker Compensation and employment The position is offered under an employment contract. Compensation is assessed individually based on professional experience and technical capability, scope of responsibility, location and relevant market benchmarks, and expected impact on the product and organization. Candidates may share their expected compensation at the beginning of the recruitment process. The compensation package consists of a base salary and equity, with flexibility for exceptional candidates. A company laptop will be provided where required for the role. Remote work and global collaboration The organization operates as a remote-first, globally distributed team. There is no fixed company-wide working schedule and no requirement to follow one specific time zone. Team members are expected to maintain sufficient working-hours overlap with their immediate colleagues to collaborate effectively. The successful candidate will work from Poland and collaborate with frontend, backend, Machine Learning and product specialists located across different regions. Poland-based employees join existing global teams rather than a separate local team. The exact reporting line and hiring manager will be confirmed during the recruitment process. How the team works Outstanding products are built by small, highly capable and hands-on teams. Decisions are made collaboratively, while individuals are expected to take ownership, bring structure to ambiguous problems and execute independently. The team moves quickly while balancing production quality, experimentation and continuous learning from real user behavior. There is no fixed hiring quota for this position; the focus is on engineers who meet the technical and ownership standards. Recruitment process The standard recruitment process consists of up to four stages: - Technical assessment, where relevant to the candidate’s background. - HR interview. - One or more technical interviews. - Founder or leadership interview. Particularly strong candidates may be fast-tracked directly to the technical interview stage based on their experience and previous work. Applications are evaluated by members of the technical team. Interviews may be conducted virtually and, where relevant, onsite. The exact format and interviewers may vary. Decisions are made efficiently, with candidates receiving a prompt outcome.

🏢 Summary: Join a cross-functional product team to build and operate scalable, high-throughput Java backend solutions for eCommerce core processes. The role combines hands-on architecture, quality ownership and product discovery, with daily use of AI-assisted and agentic coding workflows. 🗂️ Requirements: Java 21+ backend experience, Reactive or asynchronous programming experience, Virtual threads experience, Distributed backend systems experience, Event-driven messaging experience, CI/CD and automated testing experience, Code review and observability experience, AI coding tools and agentic workflow experience, Product-oriented cross-functional collaboration 📃 Skills: Java, Vert.x, Reactor, Kafka, CI/CD, DDD, SOLID, Claude, Copilot, Scrum, Kanban 🏢 Description: Description Join a cross-functional product team and build scalable solutions with real impact for customers and core business processes. Shape implementation, architecture, quality standards and product direction while contributing personality, curiosity, team spirit and a strong appetite for learning. What to expect • A collaborative cross-functional product team with a hands-on Team Lead, a Product Manager and several T-shaped Full-Stack Engineers who share ownership. • Work in Scrum or Kanban, depending on what fits the team. • Active participation in refinement, architecture and product decisions—not just implementation. • Room to shape high code quality within the team and across team boundaries. • Opportunities to improve legacy systems, complexity and grown structures step by step. What you bring Java & Backend Depth • Deep, hands-on experience with Java 21+ and modern language features, including records, sealed classes, pattern matching and structured concurrency. • Practical experience with reactive or asynchronous programming models using Vert.x, Project Reactor or comparable toolkits; understanding of non-blocking programming and when to apply it. • Experience with Java virtual threads (Project Loom) and their practical impact on concurrency models. • Experience building and operating distributed backend systems at scale, ideally in eCommerce or comparable high-throughput domains. • Comfort with event-driven communication patterns, Kafka and messaging systems, including trade-offs between synchronous and asynchronous approaches. Engineering Practices • Daily use of modern engineering practices: CI/CD, automated testing, decoupling, code reviews and observability. • Pragmatic application of DDD, Clean Architecture, Event-Driven Architecture, SOLID, feature flags and dependency injection. • Commitment to “you build it, you own it” and experience in environments that release multiple times per day. • Ability to guide the team on code quality and meaningful testing practices that catch real bugs. AI / Agentic Coding • Daily use of AI coding tools such as Claude Code or GitHub Copilot for code generation, test writing, refactoring and documentation. • Experience exploring agentic workflows, including automated PR review, scaffolding and multi-step code generation, while critically evaluating output before committing. • Understanding of AI-generated-code risks, including technical debt and loss of understanding, and enforcement of review standards. • Ability to help introduce and shape AI tooling adoption, sharing effective practices and limitations. Collaboration & Product Mindset • Knowledge sharing through pair programming, whiteboard sessions and code reviews to raise the team’s engineering level. • Close collaboration with Product Managers to understand problems, shape requirements and consider user and business perspectives. • Commitment to short iterations, rapid learning, active feedback loops and data-informed decisions. • Open, learning-oriented communication and team relationships. We are probably not a good match if you • Prefer frameworks such as SAFe or projects that begin with fixed requirements documents. • Have worked primarily in heavily regulated environments without experience in a fast-moving, product-oriented setup with distributed systems. • Prefer strict separation of infrastructure, backend, frontend, testing and product thinking. • Prefer receiving fully formed requirements rather than shaping them with Product Management. • See AI tools as a threat or gimmick rather than a productivity multiplier worth mastering. • View ongoing learning primarily as the employer’s responsibility. • Are more frustrated than energised by imperfect systems. • Keep work and good humour strictly apart.