SpaceX
Operations Engineer, Applied AI (Application Software)
About this role
SpaceX seeks an Operations Engineer to serve as a technical bridge between internal teams and AI capabilities, helping them integrate large language models into products and infrastructure. You'll design prototypes, advise on system architecture, and ensure safe, responsible deployment of LLM-powered solutions across the organization.
What you'll do
- Partner with internal teams to understand needs and design technical approaches aligned with their goals
- Act as primary technical point of contact supporting end-to-end implementation and cross-team coordination
- Build prototypes and proof-of-concept applications demonstrating effective integration with SpaceX's AI platforms
- Advise on system-to-system architecture for LLM-powered applications
- Stay current with evolving LLM technologies, best practices, and tools in fast-moving environments
- Support Sales, Product, and Engineering teams from exploration through successful implementation
What they're looking for
- Large Language Models (LLMs) and prompt engineering
- Production-level application development and coding
- System architecture and design
- LLM evaluation methods and agent systems
- Retrieval-augmented generation (RAG) and related ML tools
- Cross-functional team collaboration and communication
- AI product design, integration, and testing
- Process improvement and innovation in enterprise settings
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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 built and shipped a production application using LLMs—what were the key technical challenges you faced?
- How would you approach understanding the needs of an engineering team unfamiliar with AI capabilities and translate that into a concrete technical solution?