Research Engineer for RL Infrastructure

Key Responsibilities: Build and optimize systems infrastructure for large-scale RL and distributed training workloads Improve end-to-end training efficiency across compute, memory, networking, and scheduling layers Design and implement low-level performance optimizations, including kernels and communication paths

Required Qualifications: Strong systems engineering experience in AI/ML infrastructure, particularly for large-scale model training or inference Deep familiarity with PyTorch and distributed training frameworks such as PyTorch Distributed and DeepSpeed Experience optimizing training performance across kernels, memory movement, and parallelization strategies Hands-on experience with large-scale training techniques, including data and tensor parallelism Strong understanding of GPU architecture, profiling, and performance debugging