Senior Machine Learning Engineer, Model Training and Reinforcement Learning

Nebius•Palo Alto, CA

20h•$195,200 - $262,200

About The Position

Nebius Token Factory is building AI training and model post-training capabilities for frontier model improvement. This role owns the infrastructure that makes large-scale training and reinforcement-learning experiments possible, reliable, reproducible, and efficient. It spans distributed systems, GPU performance, model-training frameworks, RL pipelines, and production engineering. The Senior Machine Learning Engineer owns substantial machine-learning work end to end. They translate ambiguous capability goals into experiments, implement and debug training and RL recipes, build supporting data and systems, and deliver measurable improvements in model quality, experiment throughput, and reliability. The role is deeply hands-on and involves independently debugging both model-behavior and distributed-training failures. Nebius is a full-stack AI cloud platform company supporting developers and enterprises from data and model training through production deployment. Listed on Nasdaq and headquartered in Amsterdam, the company has a global footprint and more than 1,500 employees, including engineering and AI R&D teams.

Requirements

Strong Python and PyTorch engineering skills, with the ability to move quickly from idea to experiment to working system.
Hands-on experience across at least two of the following: model training, post-training/RL, applied modeling, data pipelines, or large-scale ML systems.
Ability to design rigorous experiments with baselines, ablations, metrics, and failure analysis.
Practical understanding of modern LLM behavior, instruction tuning, preference optimization, and evaluation challenges.
Practical understanding of transformer training bottlenecks, memory pressure, communication overhead, and checkpointing.
Ability to reason quantitatively about model quality, throughput, utilization, reliability, cost, and research velocity.
Strong communication skills and ability to collaborate with researchers, engineers, and leadership.
Applicants must be authorized to work in the country in which they apply and provide proof of employment eligibility as a condition of hire.

Nice To Haves

Experience with LLM post-training, RL, agents, reward modeling, synthetic data, or model evaluation.
Experience with RL frameworks or pipelines such as verl, slime, AReaL, OpenRLHF, TRL, or custom PPO/GRPO/RLHF systems.
Experience with Megatron-LM, DeepSpeed, PyTorch FSDP/DTensor, Ray, Slurm, or Kubernetes on large GPU clusters.
Familiarity with NCCL, CUDA, Triton, Nsight, InfiniBand/RDMA, and H100/H200/B200 clusters, or with model serving and inference optimization.
Publications, open-source contributions, or production impact in LLM post-training, RL, reasoning, coding models, synthetic data, distributed training, or evaluation.
Experience designing agent environments, tool-use tasks, or verifier-based rewards.

Responsibilities

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.
Build reward functions, judge models, verifiers, task environments, and evaluation sets for reasoning, coding, tool use, and agentic workflows.
Create synthetic data and data pipelines, including teacher-student generation, self-play, rejection sampling, filtering, and quality scoring.
Analyze model-behavior failures and turn them into targeted data, reward, or algorithm improvements.
Build and maintain distributed training and RL infrastructure using frameworks such as Megatron-LM, DeepSpeed, PyTorch FSDP/DTensor, Ray, verl, slime, AReaL, or OpenRLHF.
Implement and debug parallelism strategies, including tensor, pipeline, sequence/context, expert, and data parallelism; build reliable rollout, reward-serving, checkpointing, and experiment-orchestration components.
Profile and improve GPU utilization, memory usage, communication efficiency, training throughput, and inference/serving performance.
Design rigorous evaluations and ablations for capability, instruction following, reasoning, tool use, safety, and regression risk.
Write experiment plans, design documents, benchmark reports, and runbooks, and partner across research and platform teams.

Benefits

100% company-paid medical, dental, and vision coverage for employees and families.
401(k) plan with up to 4% company match and immediate vesting.
20 weeks of paid parental leave for primary caregivers and 12 weeks for secondary caregivers.
Remote work reimbursement of up to $85 per month for mobile and internet.
Company-paid short-term and long-term disability and life insurance coverage.
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.