Summary
✨ AI‑Generated
A senior software engineering opportunity to design and build Kubernetes-native software powering large-scale GPU-accelerated infrastructure for AI, machine learning, LLM, and HPC workloads. You will develop operators, controllers, custom resources, APIs, scheduling capabilities, and internal platform services. This is a hands-on development role for someone who understands Kubernetes internals and builds software on top of the platform rather than simply administering deployments.
Highlights
Senior engineering role focused on building Kubernetes-native software for large-scale GPU-accelerated AI and HPC infrastructure. The position offers hybrid flexibility, with full remote work potentially available and relocation support offered.
Description
Senior Kubernetes Platform Developer – GPU & AI Infrastructure
Location: Dallas, TX preferred
Work Arrangement: Hybrid, 3 days onsite / 2 days remote
Remote Flexibility: Full remote may be considered for the right candidate
Relocation: Available
Employment Type: Direct Hire
Overview
We are seeking a Senior Kubernetes Platform Developer to design and build the software powering a next-generation GPU-accelerated compute platform supporting AI, machine learning, LLM, and HPC workloads.
This is a software development role focused on Kubernetes, not a traditional DevOps, SRE, or Kubernetes administration position.
The core focus is developing Kubernetes-native software including custom operators, controllers, CRDs, APIs, scheduling capabilities, and internal platform services used to orchestrate large-scale GPU infrastructure.
The ideal candidate is a strong developer who understands Kubernetes internals and has experience building software on top of Kubernetes, not simply deploying applications or maintaining clusters.
Key Responsibilities
Develop Kubernetes-native software using Go, Python, or similar languages.Build custom operators, controllers, CRDs, APIs, and platform services.Extend Kubernetes to support GPU-intensive AI/ML and HPC workloads.Develop automation for cluster provisioning, lifecycle management, scheduling, and infrastructure orchestration.Build GPU scheduling, allocation, workload placement, and resource-isolation capabilities.Integrate NVIDIA technologies including GPU Operator, device plugins, MIG, and DCGM.Develop internal tools and APIs for provisioning and managing GPU compute resources.Improve platform scalability, GPU utilization, workload performance, and reliability.Integrate Kubernetes with high-performance networking, storage, and bare-metal infrastructure.Build observability and automated remediation capabilities for distributed compute environments.Required Qualifications
Strong software development experience with Go, Python, or another modern programming language.Hands-on experience building Kubernetes operators, controllers, CRDs, APIs, or other Kubernetes-native software.Deep understanding of Kubernetes architecture, controllers, reconciliation, scheduling, RBAC, networking, and cluster lifecycle.Experience developing platforms or distributed systems built on Kubernetes.Experience with GPU infrastructure and NVIDIA technologies.Experience supporting AI/ML, LLM, HPC, or other compute-intensive workloads.Strong Linux and distributed systems knowledge.Experience with Terraform, Helm, Kustomize, Argo CD, Flux, or similar tooling.Ability to troubleshoot across Kubernetes, compute, networking, storage, GPUs, and applications.Preferred Qualifications
Experience with NVIDIA GPU clusters.Experience with Slurm, Volcano, kube-scheduler extensions, or custom scheduling.Familiarity with CUDA, NCCL, PyTorch, or TensorFlow.Experience with InfiniBand, RDMA, RoCE, or high-performance networking.Experience with bare-metal Kubernetes.Experience building internal developer platforms or self-service infrastructure.Background in AI infrastructure, HPC, cloud infrastructure, or large-scale distributed systems.Ideal Candidate
The ideal candidate is a platform developer who builds Kubernetes-native systems.
This person should be comfortable writing operators, controllers, APIs, schedulers, and automation that extend Kubernetes and manage complex GPU infrastructure.
Candidates whose experience is primarily DevOps, CI/CD, Terraform administration, application deployment, or Kubernetes operations without substantial software development experience are unlikely to be the right fit.
Dallas-based candidates are preferred, but full remote may be considered for candidates with exceptional Kubernetes development and GPU infrastructure experience.
