ML Systems Engineer, Large-Scale Model Training & RL Infrastructure
About The Position
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. A Senior ML Systems Engineer owns substantial training or RL infrastructure components end to end. They are deeply hands-on, can debug difficult distributed training failures independently, and can deliver measurable improvements in experiment throughput, stability, and GPU utilization.
Requirements
- Strong Python and PyTorch engineering skills.
- Hands-on experience with distributed model training, large-scale ML systems, or GPU cluster workloads.
- Practical understanding of transformer training bottlenecks, memory pressure, gradient/optimizer state, communication overhead, and checkpointing.
- Experience debugging production or research training jobs across multiple GPUs or nodes.
- Ability to reason quantitatively about throughput, utilization, memory, reliability, cost, and research velocity.
- Strong communication skills and ability to collaborate with researchers, ML engineers, platform engineers, and leadership.
Nice To Haves
- Experience with Megatron-LM, DeepSpeed, PyTorch FSDP/DTensor, Ray, Slurm, Kubernetes, or large internal training platforms.
- Experience with RL infrastructure frameworks such as verl, slime, AReaL, OpenRLHF, TRL, or custom PPO/GRPO/RLHF systems.
- Familiarity with NCCL, CUDA, Triton, Nsight, InfiniBand, RDMA, RoCE, H100/H200/B200 clusters, or storage/network bottlenecks.
- Experience supporting SFT, DPO, PPO, GRPO, RLAIF, reward model serving, rollout generation, or agent training workloads.
- Open-source contributions to distributed training, RL infrastructure, PyTorch, Ray, Megatron, DeepSpeed, or related systems.
Responsibilities
- Build and maintain distributed training infrastructure for SFT, continued pretraining, preference optimization, and RL workloads.
- Integrate and extend frameworks such as Megatron-LM, DeepSpeed, PyTorch FSDP/DTensor, Ray, verl, slime, AReaL, OpenRLHF, or equivalent internal systems.
- Implement and debug parallelism strategies including tensor, pipeline, sequence/context, expert, and data parallelism.
- Build reliable rollout, reward model serving, replay/data buffer, checkpointing, evaluation, and experiment orchestration components for RL training.
- Profile and improve GPU utilization, communication efficiency, memory usage, and training throughput.
- Diagnose failures across NCCL, CUDA, PyTorch, Ray, schedulers, storage, networking, and checkpointing layers.
- Create reproducible training runs, launch scripts, dashboards, runbooks, and operational tooling for research users.
- Partner with research scientists to turn algorithmic training recipes into scalable, debuggable systems.
- Write clear design docs, incident reports, benchmark reports, and operating guides.
Benefits
- 100% company-paid medical, dental, and vision coverage for employees and families.
- Up to 4% company match with immediate vesting.
- 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers.
- Up to $85/month for mobile and internet.
- Company-paid short-term, long-term 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
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
