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Senior ML Engineer

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Senior/Staff ML Engineer on Nebius's AI R&D team doing applied LLM research: running experiments on training models from environment-interaction traces, reinforcement learning for reasoning and agentic systems, guided generation/search, web-scale data mining for post-training, and efficient model distillation.

Contact sales ← All open roles Senior ML Engineer (AI Research) Amsterdam, Netherlands; Israel; Remote - Europe; United Kingdom About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role This role is for Nebius AI R&D, a team focused on applied research in AI. Examples of applied research that we have recently published include: The results often lead to collaboration with adjacent teams where our research findings are applied in practice. We are currently looking for senior- and staff-level ML engineers to work on research in areas such as: - Guided search and reinforcement learning for agentic systems - Reinforcement learning for reasoning models - Web-scale problem collection for training agents - Efficient model distillation Some examples of what your responsibilities might include are: - Conducting experiments to figure out efficient ways to train a large language model on traces of interactions with various environments - Exploring methods of guided generation and search in the trajectory space - Coming up with ways to mine relevant data at web scale and figuring out efficient ways to use this data in model post-training - Conducting experiments with different reinforcement learning configurations in verifiable domains - Explori...

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