About Nebius: 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.

The role 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.

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.

Must-haves : 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 - have s : 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.

Benefits

in the US: Health insurance: 100% company-paid medical, dental, and vision coverage for employees and families.

ML Systems Engineer, Large-Scale Model Training & RL Infrastructure