SpaceX
Application Software Engineer, Applied AI
About this role
SpaceX seeks an Application Software Engineer to design and deploy AI systems—including LLMs and agentic workflows—that enhance production, flight, and Starlink operations. You'll own the full lifecycle of AI capabilities from prototype to production, partnering with product and engineering teams to integrate cutting-edge AI solutions into mission-critical systems.
What you'll do
- Develop reliable AI-powered applications that accelerate SpaceX production, flight operations, and Starlink services
- Own end-to-end lifecycle of AI systems: design, evaluation, deployment, monitoring, and iteration from prototype to production
- Architect production systems for agentic workflows, tool-use, multi-agent orchestration, and advanced retrieval with emphasis on reliability and safety
- Build rigorous evaluation frameworks and production observability systems for monitoring AI quality, safety, latency, and cost
- Identify 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
- Full-stack software development
- Large language models and agentic systems
- Python, Java, Go, C#.NET, or Scala
- Production AI deployment and integration
- Kubernetes, Docker, and data streaming (Kafka)
- Database design (PostgreSQL, SQL Server)
- Frontend frameworks (React, Angular)
- Production system architecture and monitoring
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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 time you took an AI/ML model from prototype to production—what were the biggest challenges in evaluation and deployment?
- How have you designed evaluation frameworks or monitoring systems for AI applications in production?