Research Engineer - RL Infrastructure

Skills

About the Role

You will build and optimize the systems infrastructure behind large-scale reinforcement learning and distributed training workloads. You will improve efficiency across compute, memory, networking, and scheduling, implement low-level optimizations, develop RL training systems, and collaborate with researchers on frontier-scale model training.

Requirements

• AI/ML infrastructure engineering experience
• Large-scale model training or inference experience
• PyTorch
• PyTorch Distributed
• DeepSpeed
• FSDP
• Megatron
• vLLM
• Ray
• Training performance optimization
• Data parallelism
• Tensor parallelism
• Pipeline parallelism
• GPU architecture
• Profiling
• Performance debugging
• CUDA
• Triton
• Compiler optimization
• Runtime optimization
• RL training infrastructure
• Rollout systems
• Asynchronous training pipelines
• Multi-node GPU clusters
• High-performance networking
• Open-source contributions

Responsibilities

• Build and optimize systems infrastructure for large-scale RL and distributed training workloads
• Improve training efficiency across compute, memory, networking, and scheduling layers
• Design and implement kernel, communication path, and runtime optimizations
• Develop distributed training systems for data, tensor, and pipeline parallel workloads
• Shape the architecture of the RL training stack
• Contribute to open-source libraries and internal infrastructure
• Translate system bottlenecks into concrete improvements
• Track advances in training systems, inference systems, compiler tooling, runtime tooling, and hardware-aware optimization techniques

Benefits

• Equity
• Flexible work arrangements
• Remote or in-person work options
• Visa sponsorship
• Relocation support
• Quarterly team offsites
• Hackathons
• Conferences
• Learning opportunities