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Senior ML Engineer (AI Research)

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Remote from
Europe, Israel
Salary
Undisclosed
Employment
Full Time
Experience
Senior
Published
Apply before
26 Nov 2026
Listing views
23
Application actions
2
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AI Summary

The role, at a glance.

Nebius is seeking a senior or staff-level machine learning engineer for applied AI research focused on agentic systems, language models, and reinforcement learning. The role involves designing statistically rigorous experiments, training large models across multiple compute nodes, and translating research findings into practical collaboration with adjacent engineering teams. Key research areas include guided search, reinforcement learning for reasoning, web-scale data collection, LLM post-training, and model distillation. Candidates need deep ML and RL theory, strong Python and JAX engineering skills, distributed-training experience, and the ability to communicate research results clearly. This is a high-impact role in a fast-moving AI infrastructure and research environment.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThis is a senior research-engineering position requiring advanced expertise in reinforcement learning, LLM training, distributed systems, and experimental methodology. The candidate must independently formulate research hypotheses and deliver reliable results in a rapidly evolving technical domain.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$210,000
US market range (per year)$170k–$280k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. The displayed figures are estimated annual USD compensation for a senior ML research engineer in the US market and are not employer-provided compensation.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you design an experiment to compare reinforcement-learning configurations for an LLM in a verifiable domain?

I would first define a clear task distribution, success metric, compute budget, and baseline supervised model. I would control random seeds and major hyperparameters, run repeated trials, and report confidence intervals alongside aggregate reward and task-success metrics. I would then analyze failure modes, reward hacking, training stability, and generalization to held-out tasks before selecting a configuration.

What challenges arise when training large language models across multiple computational nodes, and how would you address them?

Major challenges include communication overhead, optimizer-state memory, fault tolerance, data-loading bottlenecks, and reproducibility. I would use appropriate parallelism strategies, such as data, tensor, or pipeline parallelism; memory-efficient sharding such as FSDP or ZeRO; profiling to identify bottlenecks; and robust checkpointing. I would also ensure deterministic experiment configuration and detailed tracking of infrastructure and training metrics.

How would you approach collecting web-scale data for training software-engineering agents?

I would begin with a precise definition of useful agent trajectories and target task types. The collection pipeline would include source-quality filters, deduplication, licensing and privacy review, contamination checks, repository and issue-context extraction, and automated validation of task solvability. I would continuously evaluate data quality using downstream agent performance and targeted human or programmatic audits.

Explain the trade-offs between PPO and DPO for LLM post-training.

PPO directly optimizes a reward signal and can support complex online interaction settings, but it is operationally more complex and sensitive to reward-model quality and optimization stability. DPO is typically simpler because it learns directly from preference pairs without an explicit online RL loop, making it efficient for offline preference optimization. The preferred method depends on the availability of reliable preference data, the need for environment interaction, and the desired level of control over reward optimization.

How do you ensure that research findings are communicated clearly and can be reproduced by engineering partners?

I document the research question, assumptions, datasets, model configurations, code versions, evaluation protocol, and statistical results from the start. I provide concise experiment reports with charts, ablation results, limitations, and a recommendation that distinguishes evidence from hypothesis. For handoff, I create reproducible training scripts, pinned environments, test coverage, and clear success criteria for production validation.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Opportunity details

About this role.

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
  • Exploring methods to train AI agents on tasks with non-verifiable reward signals

We expect you to have:

  • A profound understanding of theoretical foundations of machine learning and reinforcement learning
  • Deep expertise in modern deep learning for language processing and generation
  • Substantial experience with training large models on multiple computational nodes
  • Strong software engineering skills (we mostly use python)
  • Deep experience with modern deep learning frameworks (we use jax)
  • Strong communication and leadership abilities
  • Experience designing, executing, and analyzing machine learning experiments with proper statistical rigor
  • Ability to formulate research questions, design experiments to test hypotheses, and draw meaningful conclusions from results
  • Ability to document research findings clearly and contribute to technical publications or report

Nice to have:

  • Experience with deep reinforcement learning for LLMs, including techniques such as reward modeling, DPO, PPO etc
  • Familiarity with important ideas in LLM space, such as RoPE, ZeRO/FSDP, Flash Attention, quantization
  • Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field Master’s or PhD preferred
  • Track record of building and delivering products (not necessarily ML-related) in a dynamic startup-like environment
  • Experience in engineering complex systems, such as large distributed data processing systems or high-load web services
  • Open-source projects that showcase your engineering prowess
  • Excellent command of the English language, alongside superior writing, articulation, and communication skills
  • Proficiency in contemporary software engineering approaches, including CI/CD, version control and unit testing

Benefits & Perks:

  • Competitive compensation
  • Career growth and learning opportunities
  • Flexibility and ownership
  • Collaborative and innovative culture
  • Opportunity to work on impactful AI projects
  • International environment and talented teams

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.

Apply now >

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