Staff / Principal Machine Learning Engineer, Serving - USA

with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and reliably handling thousands of concurrent connections.

Public work.

Non-trivial systems programming projects, open-source contributions to major inference engines, or deep-dive technical write-ups.

Full-cycle ownership.

You can take a model from the research team, containerize it, optimize its serving, and ensure it runs reliably in production.

Background.

PhD in CS, Physics, Math, or equivalent practical experience building backend or ML systems.

You don’t need a roadmap to start walking; you’re comfortable picking a direction and building the map as you go.

You believe engineering isn't finished until it’s shipped and stable.

You have a bias for impact over purely theoretical optimizations.

You don't just ship code; you obsess over the why.

You’re the first to question an architecture if you think there’s a better way to solve the core latency or throughput problem.

You aren't satisfied with "the PM said so." You thrive on deep context and want to understand the fundamental logic behind every decision we make.

We hand you unclear problems and expect you to make them clear.

We value engineers who say "I don't know yet" and then design the benchmark or prototype that finds out.

We treat performance, latency, and reliability as first-class product features, not a box to check before launch.

Impact comes before everything else, though we support sharing work and open-source contributions that move the field forward.

Your work should be visible.

Flat structure, fast iterations, minimal process theater.

We believe in the power of in-person collaboration to solve the hardest problems and foster a strong team culture.

We offer relocation assistance and look forward to you joining us in our Mountain View office.

The base salary range for this full-time position is $270,000 - $500,000+ bonus + equity + benefits.