Senior ML Engineer (AI Research)

<div class="content-intro"><p><strong>About Nebius:</strong></p> <p>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.</p> <p>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.</p> <p>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.</p></div><h3><strong>The role</strong></h3> <p>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:</p> <ul> <li><a href="https://arxiv.org/abs/2508.03501">applying reinforcement learning</a> for agent training in long-context multi-turn scenarios</li> <li>dramatically <a href="https://arxiv.org/abs/2505.20411">scaling</a> task data collection to power reinforcement learning for SWE agents</li> <li>building a <a href="https://swe-rebench.com/">decontaminated evaluation</a> for SWE agents that is regularly updated</li> <li><a href="https://arxiv.org/abs/2505.13652">investigating</a> how test-time guided search can be used to build more powerful agents</li> </ul> <p>The results often lead to collaboration with adjacent teams where our research findings are applied in practice.</p> <p><strong>We are currently looking for senior- and staff-level ML engineers to work on research in areas such as:</strong></p> <ul> <li>Guided search and reinforcement learning for agentic systems</li> <li>Reinforcement learning for reasoning models</li> <li>Web-scale problem collection for training agents</li> <li>Efficient model distillation</li> </ul> <p><strong>Some examples of what your responsibilities might include are:</strong></p> <ul> <li>Conducting experiments to figure out efficient ways to train a large language model on traces of interactions with various environments</li> <li>Exploring methods of guided generation and search in the trajectory space</li> <li>Coming up with ways to mine relevant data at web scale and figuring out efficient ways to use this data in model post-training</li> <li>Conducting experiments with different reinforcement learning configurations in verifiable domains</li> <li>Exploring methods to train AI agents on tasks with non-verifiable reward signals</li> </ul> <p><strong>We expect you to have:</strong></p> <ul> <li>A profound understanding of theoretical foundations of machine learning and reinforcement learning</li> <li>Deep expertise in modern deep learning for language processing and generation</li> <li>Substantial experience with training large models on multiple computational nodes</li> <li>Strong software engineering skills (we mostly use python)</li> <li>Deep experience with modern deep learning frameworks (we use jax)</li> <li>Strong communication and leadership abilities</li> <li>Experience designing, executing, and analyzing machine learning experiments with proper statistical rigor</li> <li>Ability to formulate research questions, design experiments to test hypotheses, and draw meaningful conclusions from results</li> <li>Ability to document research findings clearly and contribute to technical publications or report</li> </ul> <p><strong>Nice to have:</strong></p> <ul> <li>Experience with deep reinforcement learning for LLMs, including techniques such as reward modeling, DPO, PPO etc</li> <li>Familiarity with important ideas in LLM space, such as RoPE, ZeRO/FSDP, Flash Attention, quantization</li> <li>Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field Master’s or PhD preferred</li> <li>Track record of building and delivering products (not necessarily ML-related) in a dynamic startup-like environment</li> <li>Experience in engineering complex systems, such as large distributed data processing systems or high-load web s