ML Systems Engineer

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. Your 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-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. 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 #J-18808-Ljbffr