Kubernetes Platform Engineer, Infrastructure (GroqMetal)

Groq•New York, NY

4d•$184,800 - $302,500•Hybrid

About The Position

GroqMetal is building an infrastructure platform that turns large-scale physical compute into a reliable, isolated, customer-consumable product. The Kubernetes Platform Engineer will build and operate production software within the GroqMetal platform, helping deliver Kubernetes as a commercial product for customers running large-scale AI training and inference workloads. The engineer will own meaningful platform components, work with experienced infrastructure engineers, and develop expertise in Kubernetes, distributed systems, and large-scale AI infrastructure. The role is based in the Dallas, San Francisco, or New York City area. Remote work is available while the local Groq office is being established, with an expectation that the role will transition to onsite once the office opens. Groq describes its compensation as Total Cash, incorporating potential bonus value into base pay. The U.S. total cash salary range is $184,800–$302,500, with individual placement based on geographic location, experience, skills, and internal compensation standards. International compensation varies by local market. The company also offers a Long-Term Incentive (LTI) Program and employee benefits.

Requirements

Experience designing, implementing, testing, shipping, and maintaining production software.
Hands-on experience building or operating Kubernetes-based systems, such as controllers or operators, CRDs, cluster lifecycle, node management, scheduling, networking, or platform services.
Experience operating production software and debugging issues across application, Kubernetes, networking, compute, and infrastructure layers.
Understanding of distributed-systems concepts including failure handling, retries, idempotency, consistency, reconciliation, availability, and recovery.
Comfort with code review, automated testing, CI/CD, observability, deployment, and production operations.
Ability to independently solve well-scoped problems, ask questions when requirements are unclear, and work with senior engineers on broader architectural decisions.
Interest in developing deeper expertise in distributed systems and large-scale AI infrastructure.

Nice To Haves

Experience building Kubernetes, cloud infrastructure, or customer-consumed platform capabilities.
Substantive engineering experience using coding agents such as Codex, Claude Code, or similar tools.
Experience with hyperscalers, neoclouds, GPUs, accelerator infrastructure, or AI workloads.

Responsibilities

Build production Kubernetes platform capabilities, including APIs, controllers, operators, automation, and platform services.
Own defined platform capabilities from design and implementation through testing, deployment, operation, and continuous improvement.
Operate built systems by improving reliability, observability, performance, and operational efficiency, and by participating in production issue diagnosis and resolution.
Develop capabilities for a commercial Kubernetes platform used by dedicated customers running large-scale AI training and inference workloads.
Build distributed systems software that handles retries, failures, concurrency, reconciliation, and changing infrastructure state correctly.
Identify reliability, scalability, and usability problems and work with the team to deliver durable solutions.
Collaborate with engineers across infrastructure, inference, networking, information security, and related teams.
Contribute to design discussions, engineering practices, and technical decisions while growing toward ownership of increasingly complex platform systems.