Software Engineer (AI Data Engineering)

SpaceX•Hawthorne, CA

11h•$125,000 - $195,000

About The Position

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.

Requirements

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

Nice To Haves

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 disciplines
Self-directed and comfortable owning ambiguous problems from initial idea through long-term maintenance

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