Software Engineer (AI Data Engineering)

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 Prioritize work by return on investment and track the engineering hours saved BASIC QUALIFICATIONS: Bachelor's degree in computer science, data science, engineering, math, or physics; OR 4+ years of professional experience building automation for practical engineering use cases in lieu of a degree 1+ years of experience in software engineering with a focus on automation tools and/or AI integrations (professional and personal projects are applicable) 1+ years of programming experience in Python PREFERRED SKILLS AND EXPERIENCE: Track record of building tools that engineers or other end users actually adopted, including gathering requirements directly from users and iterating on their feedback Comfort processing and analyzing large engineering or scientific datasets (time-series telemetry, sensor data, logs, etc.) using tools such as pandas and NumPy Built code or configuration generators that turn a source of truth (design databases, spreadsheets, schemas) into validated outputs Hands-on work building complex agentic AI systems and multi-agent workflows, including prompt and context engineering, tool/function calling, and MCP servers or agent skills Ability to design evaluations for LLM-based tools, and the judgment to know when a deterministic script is a better fit than an AI model Skilled at integrating with many systems through REST APIs, and quickly learning unfamiliar internal tools and data sources Working knowledge of relational (PostgreSQL), non-relational, and vector databases, with strong SQL skills Full-stack experience building web applications or dashboards (e.g., React/TypeScript frontends with Python backends such as FastAPI or Flask) Proficiency developing on Linux, with a solid understanding of Git, testing, CI/CD, and monitoring Familiarity deploying containerized applications using Docker and Kubernetes Familiarity with hardware engineering concepts (electrical schematics and wiring, sensors, propulsion, controls, or test and manufacturing processes), or a background as a hardware or test engineer who moved into software Strong communication skills and ability to explain technical tradeoffs to engineers in other discipli