MODEL ENGINEER

Space X
United States
5 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Compensation
$180,000.0
Working hours
Regular working hours
Job source

Tech stack

C++ (Programming Language) Software Debugging Distributed Systems Python (Programming Language) Reinforcement Learning Pytorch Large Language Models Production Code

Job description

  • Design and implement the systems backing all RL workloads at SpaceXSI, from small scale ablations to production training runs
  • Profile, debug, and optimize end-to-end training performance
  • Improve scalability and observability of the RL stack

Requirements

  • Experience writing production-quality code in Python, PyTorch, JAX, Rust, or C++, * Experience building, debugging, and optimizing efficiency of large-scale distributed systems
  • Experience with large scale LLM training infrastructure
  • Strong knowledge of reinforcement learning techniques
  • Experience with RL numerics
  • Comfortable diving into unfamiliar areas and solving problems at all levels of the stack

Benefits & conditions

$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.

About the company

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.

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