Datacenter Infrastructure Specialist

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.

We are looking for a Datacenter Infrastructure Specialist to be the operational linchpin of our global fleet. Reporting to the Manager of Infrastructure Capacity & Management, you will serve as the technical authority bridging our hardware partners and internal engineering teams.

As our Datacenter Infrastructure Specialist, you will own the technical lifecycle and operational health of Runpod’s rapidly expanding, high-density GPU fleet. You will be part of a team that acts as the technical anchor for our hardware partners—serving as their infrastructure advisor, technical translator, adopter, and incident commander. This role blends deep HPC systems engineering, advanced network troubleshooting, and process automation to ensure rock-solid uptime for the world's most demanding AI workloads.

This is a high-visibility, high-impact position where you will move beyond traditional ticket-closing. You will work directly with cutting-edge GPU cloud technologies, advanced RDMA fabrics, and modern observability stacks to solve complex hardware challenges at scale. If you want the autonomy to build automated infrastructure tooling and directly contribute to the resilience, scalability, and revenue velocity of Runpod’s global physical backbone, this is where you do it.

Responsibilities

• Hardware Validation & Benchmarking: Assist in validating new hardware, ensuring partner deployments meet Runpod’s specifications for distributed AI/ML workloads.

• Uptime & SLA Enforcement: Monitor fleet health to identify performance degradation. You will help audit downtime and provide the technical data needed to protect customer SLAs.

• AI-Driven Operations: We operate with an AI-first mindset, powering our operations with the technology we host. You will work with LLMs and AI agents to help automate network triage and generate dynamic runbooks for our fleet.

• Incident Support: Coordinate technical incident communications with clear updates, acting as a steady hand that translates outages into actionable resolutions.

• Partner Technical Support: Support the growth of our infrastructure partners.

Requirements

• Professional Background: 3–5 years of experience in infrastructure operations, systems reliability, or datacenter engineering.

• Datacenter Networking: Strong proficiency in standard datacenter networking and performance troubleshooting. Exposure to RDMA, InfiniBand, or RoCE is highly preferred.

• GPU & AI Stack: Hands-on experience with the NVIDIA Software Stack (driver installation, performance utilities) and an understanding of multi-node performance tuning.

• Systems & Diagnostics: Solid Linux system administration skills and experience with containerization (Docker). You are comfortable performing system-level troubleshooting and performance tuning at the kernel and hardware interface layers.

• Effective Communication: Clear written and verbal communication skills. You can explain hardware or networking issues to both technical partners and internal leadership.

• Operational Flexibility: As our global fleet scales, this role may require participating in an on-call rotation in the future.

• Strategic Problem-Solver: You are detail-oriented and proactive when it comes to identifying potential failures before they impact customers.

Preferred

• Startup Experience: Experience working in a fast-paced environment where you have contributed to building operational workflows.

• HPC Exposure: Experience managing or optimizing bare-metal High-Performance Computing environments at massive scale.

• Observability Tools: Experience with Grafana, Prometheus, or Datadog to monitor system health.

• Automation: Proficiency in Python, Go (Golang), or Bash to automate repetitive infrastructure tasks and interface with internal APIs.