<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 services</li>
<li>Open-source projects that showcase your engineering prowess</li>
<li>Excellent command of the English language, alongside superior writing, articulation, and communication skills</li>
<li>Proficiency in contemporary software engineering approaches, including CI/CD, version control and unit testing</li>
</ul><div class="content-conclusion"><p><strong>Benefits & Perks:</strong></p>
<ul>
<li>Competitive compensation</li>
<li>Career growth and learning opportunities</li>
<li>Flexibility and ownership</li>
<li>Collaborative and innovative culture</li>
<li>Opportunity to work on impactful AI projects</li>
<li>International environment and talented teams</li>
</ul>
<p><strong>What's it like to work at Nebius:</strong></p>
<p>Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI </p>
<p><strong>Equal Opportunity Statement:</strong></p>
<p>Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.</p>
<p>Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. </p>
<p>If you need accommodations during the application process, please let us know.</p></div>
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