About the role
SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars.
SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.
SR. SOFTWARE ENGINEER, ML PLATFORM
As a Software Engineer on ML Platform at SpaceXSI, you'll build the infrastructure that turns real product usage into better models, and keeps research moving fast on large GPU fleets. ML Platform is organized into four teams. Depending on your background, you may join any of them. We're looking for strong distributed-systems and infrastructure engineers who want to sit next to research and ship platform primitives that move the product.
RESPONSIBILITIES
- Design, build, and operate core platform systems used daily by ML researchers and product engineers
- Partner closely with research to turn recurring pain into durable infrastructure
- Own reliability, performance, and developer experience for the systems in your lane
- Ship iteratively in a flat, high-ownership environment. Measure impact, then raise the bar
BASIC QUALIFICATIONS
- Bachelor's, Master's, or PhD in computer science, machine learning, or another quantitative discipline; OR equivalent work experience
- 5+ years of industry experience working with production distributed systems (e.g., infrastructure, ingestion, data pipelines, or scheduling/orchestration, etc.)
PREFERRED SKILLS AND EXPERIENCE
- Experience owning production distributed systems at meaningful scale (ingestion, data pipelines, scheduling/orchestration, or similar)
- Experience partnering and building platforms for ML researchers and product engineers
- Thrive where ownership is high and the feedback loop is short