Kubernetes Platform Engineer, Infrastructure

Mural Group; San Francisco (CA); Remote; USD 185,000 - 303,000; Full time

Job summary

Mural Group is building a managed Kubernetes platform for AI workloads. You will own components end to end—from design to production—on a distributed, cloud-native stack. The role focuses on reliability, performance, and scale in a rapidly growing environment.

Remote work is available in the US with plans for hybrid options later. You will collaborate with cross-functional teams to drive architecture decisions and deliver robust Kubernetes-based solutions for customers running AI training and

Qualifications

• Production systems software engineering experience.
• Hands-on Kubernetes: controllers/operators, CRDs, cluster lifecycle.
• Strong fundamentals in distributed systems.
• Experience with code review, automated tests, CI/CD, and observability.

Responsibilities

• Build APIs, controllers, operators and automation to extend Kubernetes.
• Improve platform reliability, observability and performance.
• Diagnose production issues across app, Kubernetes, network and compute layers.
• Handle retries, failures, concurrency and reconciliation correctly.
• Collaborate with infrastructure, inference, networking and security teams and contribute to design decisions.

Skills

Software engineering

Kubernetes

Distributed systems

CI/CD

Observability

Code review

Tools

Kubernetes controllers

CRDs

Cluster lifecycle

Scheduling

Networking

Job description

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:

• L4: $184,800–$250,000

• L5 (Sr.): $223,600–$302,500

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 and own components end to end, from design through running them in production.

What you'll do

• Build the APIs, controllers, operators and automation that extend Kubernetes

• Improve platform reliability, observability and performance

• Diagnose production issues across the application, Kubernetes, network and compute layers

• Handle retries, failures, concurrency and reconciliation correctly

• Work with the infrastructure, inference, networking and security teams and contribute to design decisions

What you bring

• Software engineering experience on production systems

• Hands-on Kubernetes: controllers or operators, CRDs, cluster lifecycle, scheduling, networking

• Solid distributed systems fundamentals

• Comfort with code review, automated testing, CI/CD and observability

Nice to have: built customer-facing cloud or Kubernetes products; GPU or AI infrastructure; daily use of coding agents like Claude Code or Codex