Director of Infrastructure Engineering

Runpod is the AI Developer Cloud. More than one million developers, from indie researchers to teams running frontier models in production, use Runpod to experiment, train, fine-tune, deploy, and scale AI on one platform. The platform has processed more than 20 billion inference requests. We closed a $100M Series A in June 2026. We're at an inflection point for AI infrastructure, and we're building the platform the next generation of developers will depend on. We're a small, remote-first team. We take ownership seriously, move fast, and ship work that more than a million developers rely on every day. We're looking for people who care deeply, build with urgency, and want to matter at scale.

Learn more in our CEO's funding announcement: https://www.runpod.io/blog/one-million-developers .

We’re looking for a Director of Infrastructure Engineering to lead and scale Runpod’s core cloud and bare-metal environments. This role owns the critical foundational layers of our platform—Site Reliability Engineering (SRE), global networking, High-Performance Computing (HPC) networks, and distributed storage engines. You’ll build the operating rhythm, culture, and technical direction that ensures Runpod remains highly available, performant, and capable of scaling to meet massive GPU computing demands.

You will partner tightly with Product Engineering, Product, and GTM leadership to support enterprise customers. Your focus is ensuring that our underlying infrastructure provides the absolute fastest, most reliable, and lowest-latency path for customers training and running large-scale AI workloads.

Responsibilities:

Own Core Infrastructure & SRE: Lead multiple engineering teams responsible for Site Reliability Engineering, networking, and storage. Establish rigorous SRE practices, driving SLA/SLO definitions, incident response, observability, and automated remediation.

Architect HPC & Global Networking: Oversee the design, scaling, and operation of Runpod’s global network backbone, as well as ultra-low-latency HPC cluster networks. Drive the implementation and optimization of InfiniBand and RDMA over Converged Ethernet (RoCE) to support massive, multi-node GPU training workloads.

Drive Storage Engine Innovation: Direct the architecture and performance tuning of highly scalable, distributed storage systems. Ensure our storage engines can deliver the massive IOPS and throughput required to keep high-end GPUs fed with data during deep learning tasks.

Build a High-Output Org: Hire, mentor, and grow highly technical engineering managers and senior ICs (network architects, systems engineers, SREs). Create a culture of ownership, operational excellence, and craft in a remote-first environment.

Translate Scale into Strategy: Partner with Program Management and Product to forecast capacity requirements, shape technical roadmaps, and convert massive scale challenges into clear technical scopes, milestones, and measurable outcomes.

Continuously Improve Systems & Flow: Drive measurable improvements in infrastructure reliability and delivery metrics, such as deployment frequency, MTTR (Mean Time To Recovery), infrastructure as code (IaC) coverage, and system uptime.

Architectural Stewardship: Provide architectural oversight for bare-metal provisioning, virtualization layers, network fabrics, and storage clusters, ensuring seamless scalability without becoming a bottleneck for your teams.

Cross-Functional Partnership: Coordinate cleanly with product delivery and platform teams to ensure the infrastructure primitives they rely on are robust, well-documented, and highly available.

Requirements:

Engineering Leadership Experience: 7+ years leading software, infrastructure, SRE, or networking teams, including managing managers and multiple squads, with a proven record of scaling high-availability cloud environments.

Deep Infrastructure Expertise: 8+ years building and operating large-scale distributed systems, bare-metal infrastructure, or public/private cloud platforms.

HPC & Advanced Networking: Proven hands-on background or strong architectural understanding of ultra-low latency networking. Deep familiarity with InfiniBand and/or RoCE , spine-leaf architectures, and global WAN routing protocols (BGP).

Storage Systems Knowledge: Experience building, operating, or tuning high-performance distributed storage systems and parallel file systems (e.g., Ceph, Lustre, Weka, NVMe-oF) capable of handling heavy AI/ML I/O loads.

SRE / DevOps Culture: Strong foundation in reliability engineering, infrastructure-as-code (Terraform, Ansible), container orchestration (Kubernetes), and modern observability stacks.

Remote-First Operating Excellence: Experience building culture, accountability, and momentum across distributed technical teams.

Communication & Collaboration: Clear written and verbal communication, strong stakeholder management, and calm, decisive leadership during high-stakes operational incidents.

Background Check: Successful completion of a background check.

Preferred Qualifications:

Direct experience architecting and operating infrastructure specifically optimized for massive GPU clusters and AI/ML workloads.