Software Engineer, ML Platform

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.

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