SpaceX
Software Engineer (Starlink)
About this role
SpaceX seeks a Software Engineer for the Starlink Enterprise team to improve data quality in go-to-market systems and build AI agents that automate sales workflows. You'll develop integrations, create Model Context Protocol servers, and partner with sales ops to turn data cleanup and manual processes into reliable automations.
What you'll do
- Improve data hygiene across sales systems by identifying and fixing bad, duplicate, or stale records through validation and enrichment
- Build integrations connecting internal tools and external platforms to flow deal and customer context seamlessly between sales, channel ops, and operational systems
- Develop Model Context Protocol servers and interfaces that safely expose data and tools to AI assistants like Grok
- Design and ship AI agents that power Starlink Enterprise go-to-market workflows and accelerate lead-to-revenue cycles
- Instrument data quality and agent performance with monitoring, feedback loops, and metrics tied to business outcomes
- Partner with sales ops, channel operations, and enablement teams to transform recurring data problems into durable automations
What they're looking for
- Python or modern programming language development
- Data engineering and SQL
- CRM data quality and hygiene practices
- AI agents and LLM-powered application development
- API design and system integration patterns
- Model Context Protocol (MCP) or similar AI tool exposure frameworks
- Sales operations and go-to-market process knowledge
- Agent evaluation, guardrails, and production AI system safety
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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 improved data quality in a business system—what validation rules or enrichment logic did you implement?
- How have you designed APIs or integration layers to keep data consistent across multiple systems?