Engineer optimizing RL inference stack for workloads from ablations to production training. Requires experience with large-scale distributed systems, LLM inference, and proficiency in Python/C++/Rust with PyTorch/JAX/CUDA.
Salary not listed
On-site5+ YOEML Engineering
About the role
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.
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