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Runware

Generative AI platform with fast inference

Head of Infrastructure Operations

Engineering LeadershipUpdated on Nov 10
Full-timeRemote in USA

About the Job

Requirements

  • At least 15 years of infrastructure engineering and operations experience, including several years leading the function at a company operating its own hardware at scale.
  • A track record of building infrastructure operations across multiple sites, regions, or countries, including opening data centers, pods, or colocation footprints.
  • Current hands-on Linux expertise covering kernel and driver behavior, performance tuning, bare-metal provisioning, configuration management, and fleet automation.
  • Strong storage fundamentals, including NVMe, distributed and parallel file systems, object storage, and designing for model-loading and caching throughput and IOPS.
  • Strong networking fundamentals, including L2/L3 design, BGP, data center fabrics, high-bandwidth interconnects, and troubleshooting links and switches.
  • Experience hiring, leading, and retaining strong engineers, setting standards, and building an effective on-call and incident culture.
  • Daily use of AI to accelerate work such as scripting, runbooks, analysis, documentation, and debugging, and a clear view of how AI tools change small-team operations.
  • Ability to own outcomes and make decisions with incomplete information in a fast-moving, ambiguous environment, while being accountable for uptime, cost, and delivery.
  • Ability to travel to sites in the US and Europe as needed.

Responsibilities

  • Build and run global infrastructure operations across pod sites in the US and Europe, including site bring-up, rack and stack, cabling, power and cooling integration, burn-in and acceptance, steady-state operations, and decommissioning.
  • Own server, storage, and network layers across the fleet, including Linux fleet management, bare-metal provisioning and imaging, firmware and driver lifecycle, high-throughput storage for model weights and caches, and data center and inter-site networks.
  • Define site launch processes and make them faster over time by turning pod deployment into a repeatable, documented, largely automated playbook.
  • Set and meet reliability targets; own capacity planning, monitoring, alerting, on-call, incident response, and post-incident reviews; and reduce GPU, node, and network failure rates.
  • Hire, lead, and develop a high-performance team of data center, systems, and network engineers across time zones, and establish operating standards.
  • Manage colocation, power, connectivity, and hardware vendors and partners, including capacity contracts, lead times, spares, RMA flows, and site-level budgets.
  • Partner with hardware and platform engineering to ensure designs are operable at scale and feed field learnings into future servers and pods.
  • Use AI to accelerate operations by automating runbooks, triage, and tooling with LLM-based agents, and encourage the team to do the same.
  • Own operational security and compliance for physical infrastructure, including access control, hardening, asset tracking, audit readiness, and data residency requirements for in-country deployments.

Desired Qualifications

  • Experience operating large GPU fleets for AI training or inference, including NVIDIA driver, Fabric Manager, and NCCL stacks, and GPU health and failure management at scale.
  • Hands-on experience with RDMA fabrics such as InfiniBand or RoCE, 400G+ Ethernet, and monitoring tools.
  • Experience with liquid-cooled, high-density, or modular and containerized data center designs, and site-level power and cooling integration.
  • Familiarity with the hardware supply chain, including server ODMs, component lead times, inventory and spares strategy, and RMA economics.
  • Strong automation stack background, including Ansible, Terraform, PXE and image pipelines, IPMI and Redfish, and observability with Prometheus and Grafana or similar.
  • Experience with Kubernetes or Slurm on bare metal.
  • Experience building or running infrastructure to meet data residency, sovereignty, or compliance requirements across jurisdictions.

About the Company

Runware

Generative AI platform with fast inference

Runware.ai provides a fast, cost-effective platform to deploy AI-generated content through an API that hides infrastructure complexity. It runs AI workloads on custom hardware in renewable-energy data centers using the Sonic Inference Engine, with real-time WebSocket connections for low-latency, scalable performance. It differentiates itself with a proprietary engine, dedicated hardware, built-in redundancy, autoscaling, GPU allocation, and access to over 188,000 models, enabling seamless integration without ML expertise. Its goal is to democratize access to generative AI for businesses by making it easy to add AI capabilities without heavy hardware costs or specialized know-how.

Company size
51-200
Company stage
Series A
Total funding
$66M
Headquarters
London, United Kingdom
Founded
2023

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