Senior Machine Learning Engineer, Model Training and Reinforcement Learning
Palo Alto, CA$195.2k - $262.2kfull-timePosted 2mo ago
About the role
Senior ML Engineer responsible for end-to-end ML work in Nebius Token Factory, focusing on large-scale training and RL experiments, infrastructure, and optimization at the intersection of distributed systems, GPU performance, model training frameworks, RL pipelines, and production engineering.
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 (tensor, pipeline, sequence/context, expert, and data parallelism) and 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 clear experiment plans, design docs, benchmark reports, and runbooks, and partner across research and platform teams.
Qualifications
- 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.
Skills
Required:PythonPyTorch
Benefits
- Health insurance: 100% company-paid medical, dental, and vision coverage for employees and families.
- 401(k) plan: Up to 4% company match with immediate vesting.
- Parental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers.
- Remote work reimbursement: Up to $85/month for mobile and internet.
- Disability & life insurance: Company-paid short-term, long-term and life insurance coverage.
Last updated Jul 29, 2026. Interested? Apply on the company site.
About Nebius
The ultimate cloud for AI explorers
Discover the most efficient way to build, tune and run your AI models and applications on top-notch NVIDIA® GPUs.
AI CloudToken FactoryGPUKubernetesSlurmManaged servicesTerraformAPI
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