Senior ML Engineer (AI Research)

Location: Amsterdam, Netherlands; remote within Europe or the United Kingdom. Applicants must be authorized to work in the country in which they apply and provide proof of employment eligibility.

Company

Nebius builds a full-stack AI cloud platform covering data management, model training, inference, and production deployment for developers and enterprises.

What you will do

• Design, implement, train, and evaluate model routers that select models or reasoning profiles for each interaction.
• Develop portable provider and protocol abstractions, schemas, contracts, and integration layers across models, APIs, agents, tools, and memory systems.
• Build systems that discover, package, adapt, and validate agent skills across coding agents, editors, and other harnesses.
• Research portable memory, identity, context, retrieval, trajectory checkpoints, and reviewable context compaction.
• Create benchmark suites and evaluation protocols covering quality, cost, latency, reliability, safety, and portability.
• Collaborate with research, infrastructure, security, product, and engineering teams to turn findings into secure, reversible, and reliable systems.

Requirements

• Profound understanding of machine learning, large language models, or statistical decision-making.
• Deep expertise in model routing, recommender systems, agent systems, retrieval and memory, model evaluation, distributed systems, or protocol and API design.
• Experience building and evaluating language-model or agentic systems with tool use and multi-turn workflows.
• Experience designing and analyzing rigorous machine learning experiments, including held-out testing, out-of-domain generalization, uncertainty, and reproducibility.
• Strong software engineering and algorithm design skills, including excellent Python and experience with APIs, schemas, distributed services, testing, observability, code review, and CI/CD.
• Ability to reason about security, privacy, provenance, permissions, failure modes, user control, and clearly communicate technical findings.

Nice to have

• Experience with model cascades, mixture-of-experts systems, cost-aware inference, multiple model providers, or local inference stacks.
• Familiarity with agent harnesses, coding agents, function calling, tool execution, MCP, and agent-to-agent protocols.
• Experience with vector search, knowledge graphs, memory architectures, context management, benchmarking, distillation, reinforcement learning, or preference learning.
• Proficiency in TypeScript, Go, Rust, or another systems language in addition to Python.
• PhD, impactful publications, open-source contributions, or deployed AI systems.

Culture & Benefits

• Competitive compensation and career growth opportunities.
• Flexible work with significant ownership and autonomy.
• Collaborative, international environment with experienced AI and engineering teams.
• Learning opportunities and the chance to work on impactful AI projects.
• Fast-moving environment focused on technical impact, trust, and shaping the future of AI.