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
Mural Group is recruiting for this role on behalf of a client. Apply here and a Mural recruiter will be in touch.
Own and operate the Kubernetes platform for Türkiye’s products, lead the migration of relational databases from RDS to EKS, and ensure reliability, security, and compliance for production workloads.
Own and scale cloud infrastructure and Ruby on Rails applications, improving availability, performance, and observability while helping engineering teams deploy safely and respond to reliability and security incidents.
Own and operate infrastructure and security across commercial cloud, FedRAMP High, and IL5 deployments, building reliable Kubernetes platforms, GitOps workflows, and repeatable environments for engineers and AI agents.
Build and evolve the tools, shared patterns, and platform architecture that help engineering teams ship reliable software faster, while improving system performance and mentoring engineers across the organization.
Build and evolve a Backstage-based developer platform, cloud infrastructure, and safe AI-agent workflows, enabling engineering teams to ship production services with built-in guardrails and less toil.
