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David Careers

Staff Kubernetes Platform Engineer, Infrastructure

Posted yesterday

mural groupMillbrae (CA)
Computer Systems Engineers/ArchitectsComputer Systems Design Services

SENIORITY

Lead

SALARY

$341,400-$401,600

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About the role

Location: Remote (US). Must be based in Dallas, San Francisco or New York City. Remote for now, with hybrid planned further down the road.
Pay: L6 (Staff): $270,400-$365,000L7 (Sr. Staff): $341,400-$401,600 Compensation is not an issue for the right person. The role pays nearly all cash: the bonus is folded into base, and there is an equity component on top. A late-stage AI cloud company backed by NVIDIA, with a valuation in the billions, runs AI compute out of data centers around the world. It is now building a managed Kubernetes platform that turns large fleets of bare-metal compute into dedicated, isolated clusters for customers running AI training and inference. You'd be early on that platform in a hands-on Staff / Sr. Staff role, writing and shipping the core code and helping set its technical direction.
What you'll do:
  • Design and build core platform systems: APIs, controllers, operators and reconciliation loops
  • Write, review, test, debug and operate production code
  • Build a commercial Kubernetes service for dedicated customers running AI workloads
  • Design for failure handling, consistency and recovery across distributed systems
  • Define stable platform APIs that shield customers from backend changes
  • Work closely with the infrastructure, inference, networking and security teams
  • What you bring
  • A track record of shipping and maintaining production software
  • Deep Kubernetes platform experience: operators, CRDs, admission control, node lifecycle
  • Practical distributed systems knowledge: consensus, leader election, eventual consistency, fault domains
  • A rigorous SDLC: design reviews, CI/CD, staged rollouts, rollback and observability
Nice to have: delivered a Kubernetes or infrastructure control plane as a product; hyperscaler or neocloud experience running AI or GPU workloads; inference platforms, accelerators or device plugins; daily use of coding agents like Claude Code or Codex

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