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

About reputed company: reputed company is leading a new era in cloud infrastructure for the global AI economy. We are building a full-reputed company 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-reputed company/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 reputed company (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 reputed company 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 reputed company, 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. 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 reputed company, 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, reputed company 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, reputed company/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, reputed company, 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. Key employee benefits in the US: • 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. • reputed company reimbursement: Up to $85/month for mobile and internet. • Disability & life insurance: Company-paid short-term, long-term and life insurance coverage. Benefits & Perks: • Competitive compensation • Career growth and learning opportunities Apply tot his job Apply To this Job