Kubernetes Platform Engineer, Infrastructure

Themuralgroup; United States; Remote; USD 185,000 - 303,000; Full time

Benefits offered by this job

Equity compensation

Remote work

Job summary

The mural group is hiring a frontend Kubernetes platform engineer to build APIs, controllers, and automation that extend Kubernetes and operate a scalable cloud platform. You will improve reliability, observability, and performance across production systems, diagnosing issues across the application, Kubernetes, network and compute layers.

Applicants should have strong distributed systems fundamentals and experience with code reviews, automated testing, CI/CD, and observability.

Qualifications

• 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.

Responsibilities

• 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

Skills

Kubernetes proficiency

Production systems

Distributed systems

Code review & CI/CD

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