Top Remote Jobs in Amsterdam

Lead and mentor a globally distributed engineering team while contributing to coding, architecture, peer reviews, and technical decisions. Design and deliver scalable user-facing systems, promote CI/CD, testing, code quality, and DevOps practices, and improve engineering workflows. Collaborate with product, design, data, and AI specialists to shape roadmaps and integrate personalized learning capabilities. Manage engineers through regular one-to-ones, feedback, and career support.

Develop and maintain web-service features, design backend APIs and business logic, review code, and improve production infrastructure reliability, redundancy, and resilience. The role requires strong programming, database design, web-service API, AWS, Linux, Docker, Kubernetes, and continuous deployment experience, along with communication, analytical, and teamwork skills.

Build and ship backend features for enterprise Coder deployments, including database migrations, observability, reporting, auditing, security, scalability, and reliability improvements. Investigate production issues, resolve operator problems, document designs, participate in code reviews and RFC discussions, and join on-call rotations. The role requires professional Go development, concurrency knowledge, SQL expertise, debugging ability, and strong communication.

Lead Anago’s commercial growth in the Netherlands and United Kingdom, managing a two-person sales team while personally closing deals and developing strategic client relationships. Set commercial direction, expand public-sector revenue, professionalize sales processes, and collaborate with marketing and product teams to translate market insights into product improvements. Use AI tools for prospecting, pipeline analysis, and forecasting, while balancing strategic leadership with hands-on execution.

Research, fine-tune, train, and deploy large language models for real-time commentary and interactive avatar applications. Build RAG systems, agentic workflows, multimodal solutions, and production-grade inference services with monitoring and tool integration. Collaborate with ML engineers, product developers, and domain experts to create reliable, observable AI systems.

Manage day-to-day delivery for a global portfolio of approximately 30 Lean and Operational Excellence applications. Responsibilities include incident, change, release, lifecycle, compliance, budget, vendor, and service-level management. The role leads internal and contracted resources, scopes enhancement demand, coordinates delivery, supports audits, and works with global business stakeholders to execute the application roadmap in a regulated manufacturing environment.

Own end-to-end global customer implementation projects from kickoff through go-live and handover. Build project plans, establish governance, manage RAID logs, milestones, dependencies, risks, and escalations, and coordinate clients and internal teams across countries and time zones. Lead status and governance meetings, drive accountability, ensure go-live readiness, maintain documentation, and support continuous process improvement.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Build and own the architecture for Dune’s curated data lifecycle, including dependency-aware orchestration, backfills, schema evolution, retries, recovery, data quality, and alerting. The role combines backend software engineering with large-scale data systems work across Go, Kotlin, Rust, Python, and SQL. Responsibilities include designing ingestion and curation contracts, making architecture decisions, solving ambiguous problems, and leading work that other engineers can execute.

Lead the company’s Physical AI research and engineering function as a hands-on technical leader. Define research priorities, design and train Transformer-based models, establish rigorous offline and real-world evaluations, integrate systems with physical robots, and analyze failures across data, models, hardware, and environments. Build a focused team, guide technical architecture and experiments, translate customer needs into requirements, and deliver measurable improvements through deployed models, benchmarks, evaluation systems, or data products.