Runpod
Director, Infrastructure Engineering
$225K–$325K
Remote - USA · Remote · Director · Full-time · Posted Jul 9, 2026
- Experience
- 7+ years of engineering leadership and 8+ years building and operating large-scale infrastructure; the posting does not state alternatives.
- Visa sponsorship
- Unable to sponsor employment visas; welcomes qualified candidates eligible to work in the United States.
About the job
Leads Runpod’s infrastructure engineering organization and sets the technical direction for reliable, high-performance cloud, bare-metal, networking, and distributed storage systems supporting large-scale AI workloads.
What you'll do
- Lead SRE, networking, and storage teams; establish reliability practices, incident response, observability, and automated remediation.
- Oversee global and HPC network architecture and distributed storage performance for large-scale GPU workloads.
- Hire and develop technical leaders, forecast capacity, shape roadmaps, and improve infrastructure reliability and delivery metrics.
What they ask for
- 7+ years leading engineering teams, including managing managers and multiple squads, with a record of scaling highly available cloud environments.
- 8+ years building and operating large-scale distributed systems, bare-metal infrastructure, or public/private cloud platforms.
- Expertise in low-latency networking, including InfiniBand and/or RoCE, spine-leaf architectures, and BGP.
- Experience with high-performance distributed storage and parallel file systems; knowledge of Terraform, Ansible, Kubernetes, and observability stacks. Successful background check.
Nice to have
- Experience optimizing infrastructure for massive GPU clusters and AI/ML workloads; knowledge of GPU interconnects such as NVLink and datacenter topology.
- Experience scaling infrastructure teams in hyper-growth startups or recognized contributions to open-source infrastructure communities.
Pay
($225,000 - $325,000) USD base salary
Tools
InfiniBand, RoCE, BGP, Ceph, Lustre, Weka, NVMe-oF, Kubernetes
AI focus
Infrastructure for large-scale AI/ML workloads, including GPU training and inference; supporting low-latency networking and high-throughput storage.
Model-written summary · the original posting is authoritative