SpaceX
Application Software Engineer, Applied AI
About this role
SpaceX is seeking an Application Software Engineer specializing in Applied AI to design, deploy, and integrate large language models and agentic systems across mission-critical operations. You'll own AI products end-to-end—from prototype to production—supporting launch vehicle production, flight operations, and Starlink infrastructure.
What you'll do
- Develop and deploy AI-powered applications that enhance SpaceX production, flight, and Starlink operations
- Own the complete lifecycle of Applied AI systems: design, evaluation, deployment, monitoring, and iteration
- Design production architectures for agentic workflows, tool-use, multi-agent orchestration, and advanced retrieval systems
- Build evaluation frameworks (LLM-as-judge, automated benchmarks) and production observability for AI quality, safety, and cost
- Uncover high-leverage AI opportunities by deeply understanding user problems and delivering efficient solutions
- Stay current with emerging LLM techniques and rapidly integrate best practices into production systems
What they're looking for
- Large language models and agentic systems
- Full-stack AI product development and deployment
- Production AI evaluation and monitoring systems
- Python, C#.NET, Go, Scala, or Java
- Docker, Kubernetes, and data streaming (Kafka)
- Frontend frameworks (Angular, React)
- Database design (PostgreSQL, SQL Server)
- System architecture and reliability engineering
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
SpaceX
SpaceX develops advanced spacecraft and satellite systems, including the Starshield government satellite constellation and Starfall re-entry cargo capsule for global delivery. The company is hiring engineers in avionics integration, software test automation, mechanical design, and hardware reliability to validate flight-critical systems and ensure mission success.
- Website
- spacex.com
Likely interview questions
- Describe a production AI system you've shipped end-to-end. What were the biggest challenges in going from prototype to production?
- How have you evaluated LLM quality in production? Walk us through your approach to building an evaluation framework.