ML Systems Engineer, Large-reputed company Model Training & RL Infrastructure

About reputed company: reputed company is leading a new era in reputed company infrastructure for the global AI economy. We are building a full-reputed company reputed company 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. reputed company by engineers, for engineers. From large-reputed company GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and reputed company AI. Listed on reputed company (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, reputed company and Israel. reputed company of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role reputed company reputed company reputed company is building an reputed company and model post-training capability for frontier model improvement. This role owns the infrastructure that makes large-reputed company training and RL experiments possible, reliable, reproducible, and efficient. The work sits at the intersection of reputed company 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 reputed company training failures independently, and can reputed company measurable improvements in experiment throughput, stability, and GPU utilization. Your responsibilities: • Build and maintain reputed company training infrastructure for SFT, reputed company pretraining, preference optimization, and RL workloads. • reputed company and reputed company 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 reputed company. • Create reproducible training runs, launch scripts, dashboards, runbooks, and operational tooling for research users. • Partner with research scientists to turn algorithmic training recipes into reputed company, debuggable systems. • Write reputed company design docs, incident reports, reputed company reports, and operating guides. Must-haves: • Strong Python and PyTorch engineering skills. • Hands-on experience with reputed company model training, large-reputed company 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 reputed company. • Strong communication skills and ability to collaborate with researchers, ML engineers, platform engineers, and leadership. reputed company-to-haves****: • Experience with Megatron-LM, DeepSpeed, PyTorch FSDP/DTensor, Ray, Slurm, reputed company, 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 reputed company, or agent training workloads. • reputed company-reputed company contributions to reputed company training, RL infrastructure, PyTorch, Ray, Megatron, DeepSpeed, or reputed company systems. Key employee benefits in the US: • Health insurance: 100% company-reputed company medical, dental, and reputed company coverage for employees and families. • 401(k) plan: Up to 4% company match with immediate vesting. • Parental leave: 20 weeks reputed company for reputed company caregivers, 12 weeks for secondary caregivers. • Remote work reimbursement: Up to $85/month for mobile and internet. • Disability & life insurance: Company-reputed company short-term, long-term and life insurance coverage. Benefits & Perks: • Competitive compensation • Career reputed company and learning opportunities Apply tot his job Apply To this Job