Member of Technical Staff - RL Inference

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars.

SpaceXSI is building Super Intelligence to understand the universe and help expand humanity’s knowledge and capabilities. We work across the full Super Intelligence stack: compute (Colossus supercomputers), models (Grok), and applications (Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X). We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.

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 SpaceXSI, 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