Research Engineer for RL Infrastructure

Job Summary

To support the development of cutting-edge AI infrastructure, the full-time Research Engineer for RL Infrastructure will build and optimize systems for large-scale reinforcement learning and distributed training, collaborating closely with researchers and engineers in a remote work environment.

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