Senior ML Engineer — Training & RL Systems

Socket.dev; Palo Alto (CA); On-site; USD 195,200 - 262,200; Full time

Benefits offered by this job

Competitive compensation

Career growth and learning

Flexibility and ownership

Collaborative and innovative culture

Impactful AI projects

International environment

Job summary

Nebius is seeking a Senior Machine Learning Engineer to own end-to-end ML work, from strategy to concrete experiments, with hands-on debugging of training and RL pipelines. This role operates at the intersection of distributed systems, GPU performance, model training frameworks, RL pipelines, and production engineering.

You will translate ambiguous capability goals into concrete experiments, implement training and RL recipes, and deliver measurable improvements in model quality, throughput, and

Qualifications

• Strong Python and PyTorch engineering skills.
• Hands-on experience across 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, 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.

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 Megatron-LM, DeepSpeed, PyTorch FSDP/DTensor, Ray, Slurm, Kubernetes on large GPU clusters.
• Implement and debug parallelism strategies (tensor, pipeline, sequence/context, expert, and data parallelism) and build 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.

Job description

Nebius is seeking a Senior Machine Learning Engineer to own end-to-end ML work, from strategy to concrete experiments, with hands-on debugging of training and RL pipelines. This role operates at the intersection of distributed systems, GPU performance, model training frameworks, RL pipelines, and production engineering.

You will translate ambiguous capability goals into concrete experiments, implement training and RL recipes, and deliver measurable improvements in model quality, throughput, and