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
You will lead a team of engineers building the infrastructure used to train, test, and evaluate our models. This is one of the few places at SpaceXAI where infrastructure and model behavior meet directly: when something breaks, it's rarely obvious whether it's a systems bug or the model doing exactly what it was trained to do, and your team has to be good at telling the difference before they can fix it.
You'll set technical direction for how we train and evaluate models at scale, stay close enough to the code to debug alongside your team, and work daily with researchers to turn tradeoffs in latency, quality, and cost into infrastructure that actually gets built. We're hiring across a range of scope for this role, depending on experience and the size of problem you're ready to own.
Example projects include..
Building the rollout infrastructure that lets researchers run RL experiments at scale without fighting the plumbing.
Designing eval pipelines that catch regressions before they ship, and give researchers fast, trustworthy signal on whether a change actually helped.
Owning the environments in which models are trained and tested: sandboxed, reproducible, and fast enough that iteration speed isn't the bottleneck.
Bringing rigor to how the team measures quality and progress, in places where "did it ship" isn't the same as "did it work?"
Partnering with research to translate model-level tradeoffs (latency, quality, cost) into concrete infrastructure decisions.
Hiring and growing the team: sourcing, interviewing, and closing exceptional infrastructure engineers, while developing your engineers through coaching, mentorship, and high-leverage project assignments.
You may be a fit if
You've led engineering teams building infrastructure that trains, evaluates, or serves ML models in production.
You have strong infrastructure and distributed systems fundamentals: you know what reliability and performance look like under real load, not just in a design doc.
You genuinely want to stay technical: you're comfortable writing code, reviewing PRs with depth, and using tools like Cursor itself to move fast.
You’re comfortable operating in ambiguity: you ask the right questions, make sound decisions with incomplete information, and help the team find a path forward.
You have a track record of hiring and developing engineers who are better than you were at their stage.
You can talk fluently with researchers about model behavior and with engineers about systems design, and you know when a problem is actually the other team's.
Bonus: hands-on experience with RL training infrastructure, eval frameworks, or building and maintaining simulated environments for model training or testing.