Job description

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. ABOUT THE ROLE As a Software Engineer on ML Platform at SpaceXAI, 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: - Telemetry — Own the collection and serving path that turns real product use into a record research can trust; without slowing the product, and under a small, explicit policy. Client-side or high-volume ingestion experience is a plus. - ML Data Platform — Build the shared environments and pipeline substrate researchers extend, so new experiments don’t fork their own stack. - Observability — Make it easy for researchers to start, watch, and debug their own runs. - ML DevX and Systems — Shorten the path from idea to a trusted run on the research fleet. 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. We're in-person with cozy offices in North Beach, San Francisco, Palo Alto, and Manhattan, New York, complete with well-stocked libraries. WHAT YOU’LL DO - 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 YOU MAY BE A FIT IF - You have a strong background in systems / infrastructure software engineering and enjoy building platforms other engineers depend on - You've owned production distributed systems at meaningful scale (ingestion, data pipelines, scheduling/orchestration, or similar) - You're comfortable across Linux, cloud and/or bare metal, and modern orchestration (Kubernetes, Ray, or equivalent) - You like working closely with ML researchers and product engineers - You thrive where ownership is high and the feedback loop is short ESPECIALLY STRONG BACKGROUNDS BY TEAM - Telemetry: event ingestion, product analytics pipelines, OpenTelemetry / tracing, reliable data APIs - Product Data Platform: data frameworks, Spark / Flink / Ray, ML dataset and training-data infrastructure - Observability: experiment / run monitoring, debug and eval tooling, agent-friendly observability UX - ML DevX and Systems: GPU / cluster scheduling, job queues, node health, research compute developer experience APPLYING If there appears to be a fit, we'll reach out to schedule 2-3 short technicals. After, we'll schedule an onsite in our office, where you'll work on a small project, discuss ideas, and meet the team.

Requirements

Must-haves

ML Data Platform — Build the shared environments and pipeline substrate researchers extend, so new experiments don’t fork their own stack.
Observability — Make it easy for researchers to start, watch, and debug their own runs.
ML DevX and Systems — Shorten the path from idea to a trusted run on the research fleet.
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.
We're in-person with cozy offices in North Beach, San Francisco, Palo Alto, and Manhattan, New York, complete with well-stocked libraries.
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
You have a strong background in systems / infrastructure software engineering and enjoy building platforms other engineers depend on
You've owned production distributed systems at meaningful scale (ingestion, data pipelines, scheduling/orchestration, or similar)
You're comfortable across Linux, cloud and/or bare metal, and modern orchestration (Kubernetes, Ray, or equivalent)
You like working closely with ML researchers and product engineers
You thrive where ownership is high and the feedback loop is short
Telemetry: event ingestion, product analytics pipelines, OpenTelemetry / tracing, reliable data APIs
Product Data Platform: data frameworks, Spark / Flink / Ray, ML dataset and training-data infrastructure
Observability: experiment / run monitoring, debug and eval tooling, agent-friendly observability UX
ML DevX and Systems: GPU / cluster scheduling, job queues, node health, research compute developer experience
If there appears to be a fit, we'll reach out to schedule 2-3 short technicals. After, we'll schedule an onsite in our office, where you'll work on a small project, discuss ideas, and meet the team.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlkubernetes

Platform: ashby
Effort: quick