Nebius

<div>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&amp;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&amp;D.</div><h3>The role</h3>
This role is for Nebius AI R&amp;D, a team focused on applied research in AI. Examples of applied research that we have recently published include:
<ul>
<li><a href="https://arxiv.org/abs/2508.03501" rel="noopener noreferrer ugc" target="_blank">applying reinforcement learning</a> for agent training in long-context multi-turn scenarios</li>
<li>dramatically <a href="https://arxiv.org/abs/2505.20411" rel="noopener noreferrer ugc" target="_blank">scaling</a> task data collection to power reinforcement learning for SWE agents</li>
<li>building a <a href="https://swe-rebench.com/" rel="noopener noreferrer ugc" target="_blank">decontaminated evaluation</a> for SWE agents that is regularly updated</li>
<li><a href="https://arxiv.org/abs/2505.13652" rel="noopener noreferrer ugc" target="_blank">investigating</a> how test-time guided search can be used to build more powerful agents</li>
</ul>
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:
<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>
Some examples of what your responsibilities might include are:
<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>
We expect you to have:
<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>
Nice to have:
<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>Benefits &amp; Perks:
<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>
What's it like to work at Nebius:
Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI 
Equal Opportunity Statement:
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.
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. 
If you need accommodations during the application process, please let us know.</div>

Senior ML Engineer (AI Research)

<div>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&amp;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&amp;D.</div><h3>The role </h3>
Nebius Token Factory is building an AI training and model post-training capability for frontier model improvement. This role owns the infrastructure that makes large-scale training and RL experiments possible, reliable, reproducible, and efficient. The work sits at the intersection of distributed systems, GPU performance, model training frameworks, RL pipelines, and production engineering.
A Senior Machine Learning Engineer owns substantial ML work end to end. They can translate an ambiguous capability goal into concrete experiments, implement and debug training and RL recipes, build the supporting data and systems, and deliver measurable improvements in model quality, experiment throughput, and reliability. They are deeply hands-on and can independently debug both model-behavior failures and distributed training failures.
Your responsibilities: 
<ul>
<li>
Design and run model-training and post-training experiments, including SFT, continued pretraining, preference optimization (DPO/IPO/KTO), and RL methods such as RLHF/RLAIF, PPO, and GRPO.
</li>
<li>
Build reward functions, judge models, verifiers, task environments, and evaluation sets for reasoning, coding, tool use, and agentic workflows.
</li>
<li>
Create synthetic data and data pipelines, including teacher-student generation, self-play, rejection sampling, filtering, and quality scoring.
</li>
<li>
Analyze model-behavior failures and turn them into targeted data, reward, or algorithm improvements.
</li>
<li>
Build and maintain distributed training and RL infrastructure using frameworks such as Megatron-LM, DeepSpeed, PyTorch FSDP/DTensor, Ray, verl, slime, AReaL, or OpenRLHF.
</li>
<li>
Implement and debug parallelism strategies (tensor, pipeline, sequence/context, expert, and data parallelism) and build reliable rollout, reward-serving, checkpointing, and experiment-orchestration components.
</li>
<li>
Profile and improve GPU utilization, memory usage, communication efficiency, training throughput, and inference/serving performance.
</li>
<li>
Design rigorous evaluations and ablations for capability, instruction following, reasoning, tool use, safety, and regression risk.
</li>
<li>
Write clear experiment plans, design docs, benchmark reports, and runbooks, and partner across research and platform teams.
</li>
</ul>
Must-haves: 
<ul>
<li>
Strong Python and PyTorch engineering skills, with the ability to move quickly from idea to experiment to working system.
</li>
<li>
Hands-on experience across at least two of: model training, post-training/RL, applied modeling, data pipelines, or large-scale ML systems.
</li>
<li>
Ability to design rigorous experiments with baselines, ablations, metrics, and failure analysis.
</li>
<li>
Practical understanding of modern LLM behavior, instruction tuning, preference optimization, and evaluation challenges.
</li>
<li>
Practical understanding of transformer training bottlenecks, memory pressure, communication overhead, and checkpointing.
</li>
<li>
Ability to reason quantitatively about model quality, throughput, utilization, reliability, cost, and research velocity.
</li>
<li>
Strong communication skills and ability to collaborate with researchers, engineers, and leadership.
</li>
</ul>
Nice-to-haves: 
<ul>
<li>
Experience with LLM post-training, RL, agents, reward modeling, synthetic data, or model evaluation.
</li>
<li>
Experience with RL frameworks or pipelines such as verl, slime, AReaL, OpenRLHF, TRL, or custom PPO/GRPO/RLHF systems.
</li>
<li>
Experience with Megatron-LM, DeepSpeed, PyTorch FSDP/DTensor, Ray, Slurm, or Kubernetes on large GPU clusters.
</li>
<li>
Familiarity with NCCL, CUDA, Triton, Nsight, InfiniBand/RDMA, and H100/H200/B200 clusters, or with model serving and inference optimization.
</li>
<li>
Publications, open-source contributions, or production impact in LLM post-training, RL, reasoning, coding models, synthetic data, distributed training, or evaluation.
</li>
<li>
Experience designing agent environments, tool-use tasks, or verifier-based rewards.
</li>
</ul>
Key employee benefits in the US:
<ul>
<li>
Health insurance: 100% company-paid medical, dental, and vision coverage for employees and families.
</li>
<li>
401(k) plan: Up to 4% company match with immediate vesting.
</li>
<li>
Parental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers.
</li>
<li>
Remote work reimbursement: Up to $85/month for mobile and internet.
</li>
<li>
Disability &amp; life insurance: Company-paid short-term, long-term and life insurance coverage.
</li>
</ul>
 <div><div><div>Pay Transparency
We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law.</div><div>Base Compensation Range</div><div>$195,200—$262,200 USD</div></div></div><div>Benefits &amp; Perks:
<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>
What's it like to work at Nebius:
Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI 
Equal Opportunity Statement:
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.
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. 
If you need accommodations during the application process, please let us know.</div>

Senior Machine Learning Engineer, Model Training and Reinforcement Learning

<div>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&amp;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&amp;D.</div><h3>The role </h3>
Nebius Token Factory is building an AI training and model post-training capability for frontier model improvement. This role owns the infrastructure that makes large-scale training and RL experiments possible, reliable, reproducible, and efficient. The work sits at the intersection of distributed systems, GPU performance, model training frameworks, RL pipelines, and production engineering.
A Senior MLE owns substantial model and endpoint optimization projects end to end. They are deeply hands-on, can debug difficult serving problems independently, and can deliver measurable improvements without needing heavy supervision.
Your responsibilities: 
<ul>
<li>
Own optimization work for specific model families, customer endpoints, or serving backends.
</li>
<li>
Run engine comparisons and recommend practical serving configurations for specific workloads.
</li>
<li>
Debug model quality or performance regressions during production rollouts.
</li>
<li>
Optimize LLM and VLM endpoints for latency, throughput, memory efficiency, GPU utilization, quality, and cost per token.
</li>
<li>
Deploy, configure, benchmark, and extend inference engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, or similar systems.
</li>
<li>
Build and productionize model-compression workflows, including quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery.
</li>
<li>
Implement or integrate speculative decoding, draft-model approaches, KV-cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving.
</li>
<li>
Build reproducible benchmark harnesses for TTFT, TPOT, tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token.
</li>
<li>
Partner with GPU kernel engineers and platform engineers to diagnose bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers.
</li>
<li>
Write clear design docs, performance reports, rollout plans, and customer-facing technical explanations.
</li>
</ul>
Must-haves: 
<ul>
<li>
Strong Python and PyTorch engineering skills.
</li>
<li>
Hands-on experience deploying or optimizing LLM, VLM, or high-throughput transformer inference systems.
</li>
<li>
Practical knowledge of at least one modern inference stack such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or equivalent internal systems.
</li>
<li>
Strong understanding of transformer inference bottlenecks, including KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving.
</li>
<li>
Ability to reason quantitatively about latency, throughput, quality, utilization, and cost tradeoffs.
</li>
<li>
Strong communication skills and ability to collaborate with research, kernel, infrastructure, product, and customer teams.
</li>
</ul>
Nice-to-haves: 
<ul>
<li>
Experience with quantization-aware training, post-training quantization, FP8, INT8, INT4, NVFP4, MXFP4, AWQ, GPTQ, SmoothQuant, or related techniques.
</li>
<li>
Experience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or other inference acceleration methods.
</li>
<li>
Experience with agentic workloads, including tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration.
</li>
<li>
CUDA or Triton familiarity, even if the role is not primarily a kernel-engineering role.
</li>
<li>
Open-source contributions to vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, KServe, or related projects.
</li>
</ul>
Key employee benefits in the US:
<ul>
<li>
Health insurance: 100% company-paid medical, dental, and vision coverage for employees and families.
</li>
<li>
401(k) plan: Up to 4% company match with immediate vesting.
</li>
<li>
Parental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers.
</li>
<li>
Remote work reimbursement: Up to $85/month for mobile and internet.
</li>
<li>
Disability &amp; life insurance: Company-paid short-term, long-term and life insurance coverage.
</li>
</ul>
 
#LI-BH3
 <div><div><div>Pay Transparency
We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law.</div><div>Base Compensation Range</div><div>$195,200—$262,200 USD</div></div></div><div>Benefits &amp; Perks:
<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>
What's it like to work at Nebius:
Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI 
Equal Opportunity Statement:
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.
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. 
If you need accommodations during the application process, please let us know.</div>

Senior Machine Learning Engineer, LLM Inference Optimization

<div>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&amp;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&amp;D.</div>We are seeking a Senior Applied ML Engineer to join a fast-growing team building an agent-native search platform for AI systems, the emerging web access layer for AI. You will develop and deploy machine learning models that power retrieval, ranking, and indexing at scale, helping AI systems access fresh, reliable information in real time. This is a high-impact role working on a production system used 24x7, tackling challenges comparable to large-scale web search.
Your responsibilities:
<ul>
<li>Design, train, and deploy ML models for retrieval, reranking, and search relevance in production</li>
<li>Build and optimise embedding-based indexing and large-scale retrieval systems</li>
<li>Develop models supporting crawling, data selection, and content understanding</li>
<li>Define and improve quality metrics for agent-native search and build evaluation pipelines</li>
<li>Work on systems operating at very large scale, including high-throughput query workloads</li>
<li>Collaborate closely with engineering teams to integrate ML models into production services</li>
<li>Analyse performance trade-offs across latency, quality, and cost</li>
<li>Experiment with and apply state-of-the-art techniques in search, retrieval, and LLM-integrated systems</li>
<li>Contribute to product and architectural decisions in a fast-moving environment</li>
</ul>
Must-haves:
<ul>
<li>5+ years of experience in software engineering or applied machine learning</li>
<li>Strong programming skills in Python, Go, or C++</li>
<li>Proven experience deploying ML models in production systems</li>
<li>Hands-on experience with retrieval, ranking, recommendation, or similar ML problems</li>
<li>Strong understanding of machine learning and modern deep learning techniques</li>
<li>Experience working with large-scale data systems and high-throughput environments</li>
<li>Ability to design evaluation frameworks and define meaningful model metrics</li>
<li>Product-oriented mindset with a focus on impact and iteration</li>
<li>Strong problem-solving skills and ability to work in a distributed team</li>
</ul>
Nice-to-haves:
<ul>
<li>Experience with search systems or large-scale information retrieval</li>
<li>Familiarity with embeddings, transformers, and modern NLP systems</li>
<li>Experience working on LLM-powered or agent-based systems</li>
<li>Contributions to open-source projects, technical publications, or conference talks</li>
<li>Participation in competitive ML (e.g. Kaggle) or similar signals of strong technical ability</li>
</ul>
We conduct coding interviews as part of the process.
 <div>Benefits &amp; Perks:
<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>
What's it like to work at Nebius:
Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI 
Equal Opportunity Statement:
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.
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. 
If you need accommodations during the application process, please let us know.</div>

Nebius

Open jobs at Nebius

Senior ML Engineer (AI Research)

Senior Machine Learning Engineer, Model Training and Reinforcement Learning

Senior Machine Learning Engineer, LLM Inference Optimization

Senior Applied ML Engineer (Agentic Search)

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