ABOUT THE ROLE:

The RL infrastructure team is looking for an engineer to help with low precision RL training and inference.

RESPONSIBILITIES:

• Design and optimize our inference stack for all shapes of RL workloads at xAI, from small scale ablations to production training runs.
• Analyze, profile and address performance bottlenecks in large scale RL systems
• Work closely with the modelling team to efficiently implement novel RL techniques and algorithms

BASIC QUALIFICATIONS:

• Experience in building, debugging, and optimizing efficiency of large-scale distributed systems
• Experience in LLM inference
• Proficiency in programming languages such as Python, C++ and/or Rust; frameworks such as PyTorch, Jax, CUDA
• Willingness to dive deep and solve hardcore problems at all levels of the stack

PREFERRED SKILLS AND EXPERIENCE:

• Strong knowledge in quantization and numerics in LLM inference and training
• Experience in developing inference engines, e.g. SGLang, vLLM