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Senior Software Engineer - GPU Infrastructure

Harrison Clarke
United States
21 days ago
Apply on arc.dev
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Working hours
Regular working hours
Job source
arc.dev

Tech stack

Artificial Intelligence Amazon Web Services Cloud Computing Linux Virtual Private Networks (VPN) Kernel-Based Virtual Machine Network Security OpenStack PCI Express Virtualization Technology AI Infrastructure Multi-Cloud
+3 more
Kubernetes Bare Metal Hardware Infrastructure

Job description

  • Build and maintain the platform that provisions, orchestrates, and virtualizes GPU infrastructure at scale
  • Design automated AI infrastructure services across multi-cloud environments
  • Own the systems that turn raw, fragmented GPU capacity into a consistent, production-ready platform customers depend on
  • Work on novel problems at the intersection of cloud infrastructure and the GPU market - provisioning, fleet management, secure networking, and virtualization
  • Get hands-on with bleeding-edge GPU hardware and the latest AI dev tooling

Requirements

  • Strong experience with virtualization technologies - KubeVirt, OpenStack, KVM, or equivalent
  • Deep Kubernetes expertise, including building custom resources and managing controllers
  • Hands-on with GPU-specific orchestration, testing, and fleet management
  • Secure networking chops - VPNs, VPCs, and multi-tenant isolation
  • Linux depth, particularly around PCIe passthrough and driver management
  • Comfortable working in Go - though we value being language-agnostic over any single stack
  • Experience operating across AWS, GCP, and/or bare-metal infrastructure

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

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Good distractions

Talks and stories from around this role — technically off-topic, practically not.

Moments

Explore playlists

2:28 min

Understanding Kubernetes architecture and core cluster components

Marc Nimmerrichter · World Congress 2022

2:10 min

Transitioning domain specific analytics from OpenStack to Kubernetes

Ingvord Ingvord · World Congress 2024

52 sec

Running persistent Linux environments directly on Windows

Ben Breard Ben Breard · World Congress 2025

1:37 min

Unlocking direct GPU access within managed Kubernetes platforms

Kevin Klues Kevin Klues

4:04 min

Overview of Kubernetes operators and custom resource definitions

Philipp Krenn · World Congress 2022

1:29 min

Transitioning from content management to cloud infrastructure

Matt Butcher · World Congress 2023

Videos

See all
Running Secure Life Science Research at Scale using Hybrid GPU HPC and Kubernetes 🧬 thumbnail
24:23
World Congress 2026 Europe • about 2 months ago

Running Secure Life Science Research at Scale using Hybrid GPU HPC and Kubernetes 🧬

Jeremy Murray

Understanding Kubernetes in a visual way thumbnail
29:05
World Congress 2026 Europe • about 2 months ago

Understanding Kubernetes in a visual way

Aurélie Vache

How I saved 200K/yr in direct costs writing 0 code lines in K8s thumbnail
19:36
World Congress 2024 • about 2 years ago

How I saved 200K/yr in direct costs writing 0 code lines in K8s

Ingvord

Docker network without Docker thumbnail
28:26
World Congress 2025 • about 1 year ago

Docker network without Docker

Oliver Seitz

Instant KAI Sandboxes with vCluster: Multi-Tenant, Multi-Scheduler GPU Sharing thumbnail
27:54
World Congress 2026 Europe • about 2 months ago

Instant KAI Sandboxes with vCluster: Multi-Tenant, Multi-Scheduler GPU Sharing

Piotr Zaniewski

A Deep Dive on How To Leverage the NVIDIA GB200 for Ultra-Fast Training and Inference on Kubernetes thumbnail
32:19
World Congress 2025 • about 1 year ago

A Deep Dive on How To Leverage the NVIDIA GB200 for Ultra-Fast Training and Inference on Kubernetes

Kevin Klues

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