Spacex Production Engineer Jobs (NOW HIRING)

SR. SOFTWARE ENGINEER (PLATFORM TEAM) 

The Platform Team builds the foundational tooling and security infrastructure that empowers every team at SpaceX to harness AI effectively. This team creates secure, scalable gateways and proxy systems that allow engineers and operators across the company to write code faster, perform advanced data analysis, connect AI to their daily tools and problems, and turn ideas into deployed solutions with confidence. By onboarding new frontier models, expanding compute capacity, optimizing proxy efficiency for logging and control, and building shared prompts, skills, and infrastructure, this team unlocks reliable, high-impact AI capabilities across the entire organization. 

As aSr. Software Engineer for SpaceX's AI platform, you will be a key technical leader and entrepreneurial builder focused on the leanest and most effective applied use of AI at SpaceX. You will design and scale platforms that convert engineering know-how into tested software, automation, and reliable data stores at company scale while creating risk-mitigation tools such as intelligent code review and PR assistants. Current systems are built with Docker containers acting as secure gateways and proxies that integrate with any cloud compute provider and multiple frontier model providers. Your work will directly accelerate SpaceX production and development by enabling every team to move faster with trustworthy AI. 

RESPONSIBILITIES: 

• Lead the design, architecture, and implementation of secure, scalable AI platforms and proxy systems used company-wide
• Own complex platform initiatives end-to-end, including technical strategy, implementation, testing, deployment, and long-term evolution
• Drive onboarding of new frontier models, expansion of compute resources, and optimization of proxy systems for performance, security, logging, and control
• Champion adoption across SpaceX by mentoring engineers, communicating value, building shared products/prompts/skills/infrastructure, and providing hands-on support
• Act as an entrepreneurial force to identify high-value opportunities and deliver solutions that allow users to rapidly convert problems and ideas into tested software, deployed applications, and trained models on managed, reliable compute
• Develop and scale tools that mitigate business risk, such as advanced AI-powered code review, PR automation, and safety guardrails
• Define best practices for lean, effective, and secure applied AI that maximizes conversion of engineering expertise into automation and reliable data systems
• Collaborate with and influence cross-functional stakeholders, including ML researchers, security, and operations teams 

BASIC QUALIFICATIONS: 

• Bachelor's degree in computer science, computer engineering, or other engineering discipline and 5+ years of professional experience building production software; OR 7+ years of professional experience building production software in lieu of a degree
• Experience developing and operating production platforms (AI/ML infrastructure, developer platforms, or large-scale backend systems) 

PREFERRED SKILLS AND EXPERIENCE: 

• Proven success building and scaling internal AI gateways, secure proxy systems, or MLOps platforms in a fast-paced environment
• Deep hands-on experience with Docker, Kubernetes, security architecture, and integrations across multiple cloud and frontier model providers
• Strong background in model onboarding, compute orchestration, observability, and infrastructure for high-volume AI usage
• Demonstrated ability to enable and mentor others through tools, documentation, training, and direct partnership
• Entrepreneurial track record of identifying opportunities and delivering outsized impact
• Proficiency in Python and strong experience with additional languages (Go, Java, TypeScript, Rust, etc.)
• Experience with infrastructure-as-code, CI/CD, and building highly reliable distributed systems
• Passion for AI risk mitigation, developer productivity, and turning complex engineering challenges into simple, scalable AI-powered outcomes