RL Systems Engineer: Inference & Training at Scale

xAI; Palo Alto (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

xAI is seeking an engineer for the RL infrastructure team to help with low precision RL training and inference. You will design and optimize the inference stack for RL workloads, profile performance, and collaborate with the modelling team to implement novel RL techniques.

This role requires building and optimizing large-scale distributed systems and proficiency in Python, C++, or Rust, along with PyTorch, Jax, and CUDA. Strong problem-solving and deep-stack focus are essential for success.

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.

Responsibilities

• Design and optimize the inference stack for RL workloads across sizes.
• Analyze, profile and address performance bottlenecks in large-scale RL systems.
• Work closely with the modelling team to implement novel RL techniques and algorithms.

Skills

Distributed systems

LLM inference

Programming languages

Tools

Python

C++

Rust

PyTorch

JAX

CUDA

Job description

xAI is seeking an engineer for the RL infrastructure team to help with low precision RL training and inference. You will design and optimize the inference stack for RL workloads, profile performance, and collaborate with the modelling team to implement novel RL techniques.

This role requires building and optimizing large-scale distributed systems and proficiency in Python, C++, or Rust, along with PyTorch, Jax, and CUDA. Strong problem-solving and deep-stack focus are essential for success.