Software Engineer, RL Data

Speedrun Talent Network; San Francisco (CA); Hybrid; USD 150,000 - 190,000; Full time

Job summary

SpaceXAI in SF seeks a Software Engineer, RL Data to shape training data for Grok models. You will determine what problems agents practice, what worlds they train in, and how they are judged, turning weaknesses into measurable improvements.

You will build systems to test hypotheses, run ablations, and read rollouts to find root causes behind model behavior. Realistic simulations and production-like codebases power this work.

Qualifications

• Strong software engineering fundamentals and production-grade coding experience.
• Experience building data pipelines and testing infrastructures.
• Ability to design and evaluate data for ML models.
• Comfort working with complex backend systems and simulations.

Responsibilities

• Turn capability gaps into verifiable tasks within real codebases.
• Create environments that simulate production apps and data flows.
• Design graders to distinguish good solutions from shortcuts.
• Build automated checks to catch problematic data or behavior.
• Scale task generation from thousands to millions with reliable filters.

Skills

Python

Data pipelines

Test infrastructure

Sandbox environments

Backend services

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.

Software Engineer, RL Data

About the Role

The data behind a model shapes it more than almost anything else. On the RL Data team at SpaceXAI, you decide what that data is for our agents: which problems they practice, what world they practice in, and how they're judged. You'll take a weakness, like an agent that gives up on hard bugs or misuses a calendar API, and turn it into training data that fixes it, then show the next model is measurably better.

You form hypotheses about why data is helping or misleading the model, build the systems to test them, run ablations, and read the rollouts to find the bug, shortcut or mismatch behind a result. Every flaw you catch, or miss, shows up in what the next Grok models learn.

What you’ll work on

• Turning a capability gap into thousands of verifiable tasks — for example, planting subtle bugs deep in a real project's dependencies for the agent to root-cause.

• Building environments that behave like production: real codebases with their real dependencies, and simulations of apps like email, calendar, and docs that match the real ones down to search, errors, and pagination.

• Designing graders for open-ended work that separate good solutions from bad without penalizing valid alternatives or rewarding shortcuts.

• Building automated checks that catch reward hacking, leaked answers, broken environments, and paths the real product wouldn't allow, before data reaches training.

• Scaling from a thousand hand-checked tasks to millions, with filters and judges you can trust in place of a person reading each one

You may be a fit if

• You have strong software engineering fundamentals, and you've built non-trivial systems in Python: data pipelines, test or eval infrastructure, sandboxes, or backend services.

• You have taste for what makes an environment trustworthy: faithful enough that skills transfer, strict enough that shortcuts fail, and simple enough to debug when a result looks wrong.

• You are careful and systematic — understanding both aggregate metrics and close reading of raw outputs.

• You enjoy turning messy, open-ended problems into repeatable, high-quality data production systems.

• RL or ML experience is a plus but not required. We welcome strong engineers who want to do research on what makes models better.