Software Engineer

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 enabling human life on Mars.

SOFTWARE ENGINEER (AI DATA ENGINEERING)

Be a member of the Artificial Intelligence Data Engineering Software team, building AI-powered tools and automation that solve real engineering problems for our launch vehicles and spacecraft.

Our team builds software that takes recurring, time-consuming work off engineers' plates so they can focus on hard engineering, from automated review of flight and test data to AI agents that help engineers prepare for missions and investigate anomalies. Your work will directly accelerate rapid reuse of the Falcon launch vehicle, support the world's largest satellite communication and AI constellations, and contribute to the development of the world's largest rocket capable of sending humans to Mars.

You will work side-by-side with engineers across avionics, propulsion, structures, and more to find where their time goes and build tools that give it back, using agentic AI systems, MCP servers, RAG-based search, data pipelines over large telemetry datasets, and traditional automation where it is the right answer. You will own problems end-to-end: scoping the solution with the engineers who have the problem, rolling it out, measuring the time it saves, and maintaining it as the vehicle and processes evolve.

Aerospace experience is not strictly required - we are looking for smart, motivated, collaborative engineers who love solving problems and want to make an impact on an inspiring mission.

RESPONSIBILITIES:

• Partner directly with engineering teams to identify their most time-consuming, repetitive, or error-prone work, then design and build tools that automate it
• Build automation that processes large volumes of engineering data, including telemetry, hardware design databases, configuration files, and manufacturing and test records, to automate data review, flag anomalies, and surface insights
• Design agentic AI systems, multi-agent workflows, and MCP servers that let engineers query, analyze, and act on data across many internal systems
• Integrate tools into CI/CD pipelines, mission workflows, and review/sign-off processes so automation runs where engineers already work
• Build internal web dashboards that give engineers a clear view of mission status, open work, and data review results
• Rapidly prototype and iterate, then take tools to a production-grade, maintainable state
• Thoroughly test and validate all tools, including evaluating AI outputs against known-good results, so engineers can trust them in a flight-critical environment