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Research Scientist
On site · San Francisco, CA
This role is on site in San Francisco, CA.
Locations: San Francisco, CA · New York City, NY
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About the role
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 Scientist
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 Scientists who can drive effective RL or mid-training research in a small-team setting. You’ll own ambiguous, hard research problems end-to-end: forming hypotheses, designing experiments, building the training/eval/data needed to test them, and pushing results into the next model. You should expect significantly more scope and autonomy than in other research labs.
What you’ll do
Improve our understanding of RL, what it takes to handle longer horizon tasks, and train with less compute
Train graders to improve performance on coding tasks with non-verifiable reward
Improve the quality and difficulty of datapoints we use for training our models
Realtime RL for coding agents
You may be a fit if
You have a deep background in RL and strong machine learning fundamentals
You’re an excellent programmer and software engineer
You can handle ambiguous research tasks with little guidance
You care a lot about data quality, and can dive into the data when appropriate
You are truth seeking, aiming to learn more about the science than proving your ideas are correct.
#LI-DNI
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