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
One of our north stars is a future where teams can hire Grok as a remote colleague. Reaching that future requires training our models in realistic, diverse environments that support complex, end-to-end work across domains.
As a Software Engineer on the RL Environments team at SpaceXAI, you’ll build the systems that turn real-world data into high-quality environments and tasks for reinforcement learning. You’ll create dramatically more realistic training environments while owning the shared platforms and quality layers that help us produce trustworthy data quickly and at scale.
This role sits at the intersection of research, data, and engineering. You’ll work closely with ML Platform and research teams to turn company data, Grok Bot interactions, vendor-built tasks, tutor data, acquired data, and synthetic data into training-ready environments. Your work will make high-quality RL data easier to create, validate, discover, and use across our model-training efforts.
What you’ll work on
Building platforms that allows us to build complex, realistic, and diverse RL environments at scale, that support end-to-end tasks across knowledge-work domains.
Designing end-to-end factory that turns massive raw data into useful and realistic environments.
Defining and applying consistent quality standards across vendor, tutor, acquired, and synthetic data.
Creating self-serve APIs and tooling that accelerate task and environment development for internal teams and external contributors.
Partnering across research, platform, and external teams to translate model-capability goals into effective training tasks and environments.
You may be a fit if
You have strong software engineering fundamentals and experience with data platforms, developer tools, distributed systems, or ML infrastructure.
You can turn ambiguous quality standards into concrete, automated checks.
You’re comfortable building repeatable pipelines from messy, heterogeneous data.
You care about model behavior and can translate capability goals into tasks and experiments.
You collaborate well across disciplines and own open-ended problems end to end.
Applying
If there appears to be a fit, we'll reach out to schedule 2-3 short technicals. After, we'll schedule an onsite in our office, where you'll work on a small project, discuss ideas, and meet the team.