Why this role stands out
This role offers a unique opportunity to shape the future of coding by building cutting-edge ML infrastructure, perfect for engineers with a strong distributed systems background and a passion for innovation. You'll thrive in a flat, talent-dense team environment where your contributions directly impact groundbreaking research and development, so apply to join this mission!
Quick overview
Job description
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.
Research Engineer
SpaceXAI is building the future of coding. We train frontier coding agents and scale RL on real user data to make them increasingly effective.
About the role
We’re looking for Research Engineers to build the training, inference, and data systems behind our frontier coding models. You’ll work directly with researchers to make progress repeatable and iteration fast.
What you’ll do
Build our distributed training, inference, and RL infrastructure
Write libraries to simplify how researchers do large-scale data jobs
Architect the systems that turn Cursor user data into effective training data
You may be a fit if
You have a strong infrastructure/distributed systems background
You are able to architect and ship end-to-end with high ownership
You have strong intuitions about how language models work
You’re excited to learn more about ML
#LI-DNI
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