xAI seeks an engineer for its RL infrastructure team to build and optimize low precision RL training and inference systems. Responsibilities include designing the inference stack, profiling performance bottlenecks, and collaborating with the modelling team to implement novel RL techniques. Candidates should have experience with large‑scale distributed systems, LLM inference, and programming in Python, C++, or Rust, as well as familiarity with PyTorch, Jax, and CUDA. Preferred skills include quantization knowledge and experience with inference engines such as SGLang or vLLM.
Member of Technical Staff - RL Inference at xAI
On-site - Palo Alto, CA
More jobs at xAIRequirements
Skills
- Experience in building, debugging, and optimizing the efficiency of large-scale distributed systems
- Experience in LLM inference
- Proficiency in programming languages such as Python, C++ and/or Rust
- Experience with frameworks such as PyTorch, Jax, CUDA
- Willingness to dive deep and solve hardcore problems at all levels of the stack
- Strong knowledge in quantization and numerics in LLM inference and training
- Experience in developing inference engines, e.g. SGLang, vLLM
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
Technologies
PythonC++RustPyTorchJaxCUDASGLangvLLM
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