Member of Technical Staff - RL Inference

SpaceXSI; Palo Alto (CA); On-site; USD 180,000 - 440,000; Full time

Job summary

SpaceXSI in Palo Alto is seeking a Member of Technical Staff for RL inference. You will help design and optimize our RL inference stack and support low precision training and inference across workloads, from experiments to production runs.

You will profile and address performance bottlenecks in large-scale RL systems, and collaborate with the modelling team to implement novel RL techniques and algorithms using Python, C++, Rust and relevant ML frameworks.

Qualifications

• Experience in building, debugging, and optimizing efficiency of large-scale distributed systems.
• Experience in LLM inference.
• Proficiency in Python, C++ and/or Rust; frameworks such as PyTorch, Jax, CUDA.

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

Skills

Distributed systems

LLM inference

Python

C++

Rust

Performance profiling

Optimization

PyTorch

JAX

CUDA

Tools

PyTorch

JAX

CUDA

SGLang

vLLM

Job description

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 help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including 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

COMPENSATION AND BENEFITS:

$180,000 - $440,000 USD

Base salary is just one part of our total rewards package at SpaceXSI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks.