Senior Machine Learning Engineer, Model Training and Reinforcement Learning

Навыки

• Коммуникация
• CUDA / ONNX / TensorRT
• Распределённые системы
• Дообучение моделей
• Kubernetes
• Лидерство
• LLM

О компании и продукте

• 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.
• 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.

Требования

• 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: 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
• Nice - to - have s
• 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
• Key employee benefits in the US
• 401(k) plan: Up to 4% company match with immediate vesting
• Parental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers

Условия

• Health insurance: 100% company-paid medical, dental, and vision coverage for employees and families
• Remote work reimbursement: Up to $85/month for mobile and internet
• Disability & life insurance : Company-paid short-term, long-term and life insurance coverage

Паспорт вакансии

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