> Markdown version of [/jobs/ext/4619008-infrastructure-engineer](https://www.wearedevelopers.com/jobs/ext/4619008-infrastructure-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Infrastructure Engineer

- **Company:** Foundry Robotics
- **Location:** San Francisco, CA, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Computing Platforms, Continuous Integration, Software Debugging, Linux, Domain Name System (DNS), Virtual Private Networks (VPN), Python (Programming Language), Machine Learning, Routing, Packet Analyzer, AI Infrastructure, Transport Layer Security, Kubernetes, Terraform
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25197_d48b824b321f78b06773cd6dfc0a5776

## About the Role

* 3+ years running production infrastructure with hands-on Kubernetes.
 * Fluent in Terraform and GitOps. You have worked in infrastructure CI/CD where merge means apply and you know how to make that safe.
 * Solid networking fundamentals: VPCs, routing, VPN, DNS, TLS, overlay networks, and the ability to debug a routing problem with a packet capture.
 * Comfortable in Go or Python for tooling and glue, and able to read production service code well enough to debug it.
 * Experience operating an observability stack and a track record of making alerts trustworthy.
 * Security-minded by default: least-privilege access, no public endpoints, secrets from config, and the reflexes to catch a committed credential., * Experience running Kubernetes on-prem.
 * Edge or fleet experience: arm64 Linux devices, over-the-air rollout, offline-tolerant design.
 * WireGuard-based overlay networks at organization scale.
 * Time spent near robots or industrial hardware.
 * Experience in an ITAR or CMMC environment.

## Description

You will work on the infrastructure that our services, our factory-floor software, and our ML training run on: AWS, Kubernetes, the network boundary between them, the deployment pipeline, GPU workload scheduling, and the monitoring that tells us when something is wrong.

This is a unique position with huge scope, spanning traditional containerized software platforms, as well as robotics, networking and AI infrastructure. Unlike an internet company, you will work on capabilities that support real hardware, and unlike traditional robotics, this platform also orchestrates the manufacturing process and the continued operation of the robots themselves.

It is a small team with a lot of surface area. Expect to move between infrastructure as code, cluster operations, GPU scheduling, and edge-device support in the same week.