About Runware
Runware is the AI inference platform that builds and operates its own infrastructure, end to end. One API gives developers access to image, video, audio, 3D and language models, and our Sonic Inference Engine runs them on hardware we designed ourselves: custom servers, custom boards and a closed-loop, water-free cooling system, packed into Sonic Inference Pods — roughly 1 MW of liquid-cooled GPU compute in a 20-foot container that can be sited wherever power is cheap and available.
This is why infrastructure at Runware is the product, not a cost center. Our edge is that we run inference 30–90% cheaper per GPU-hour than our peers, and that edge is earned in the data center: in how fast we can stand up a new site, how tightly we run our fleet, and how little downtime our customers ever see. More than 20 billion requests have run through the platform for over a million developers, with customers such as Wix, Quora, Freepik and Higgsfield building production applications on top of it.
We raised a $50M Series A led by Dawn Capital, with Comcast Ventures, Speedinvest, Insight Partners and a16z speedrun participating, and we are now building out a global network of pods across the US and Europe. We need someone to own how that network is operated.
The role
As Head of Infrastructure Operations, you'll own the physical and operational backbone of Runware: the servers, storage and networking across every site we run, and the team that keeps them running. You'll take us from a handful of pods to a global fleet, and you'll decide how that fleet is built, provisioned, monitored, maintained and scaled.
This is a hands-on leadership role. You'll set the operating model and hire the team, but you'll also be in the terminal, in the rack and in the incident channel when it matters. The people who do best here are senior operators who never stopped being engineers, who are already using AI tools to multiply their own output, and who know what a high-performance infrastructure team looks like because they've built one before.
You'll report to Risto Haukioja, Head of AI Infrastructure and work closely with the hardware, platform and inference engineering teams, and with our data center, power and hardware partners.
What you'll do
- Build and run global infrastructure operations across our pod sites in the US and Europe: site bring-up, rack and stack, cabling, power and cooling integration, burn-in and acceptance, through to steady-state operations and decommissioning.
- Own the server, storage and network layers of the fleet end to end: Linux fleet management, bare-metal provisioning and imaging, firmware and driver lifecycle, high-throughput storage for model weights and caches, and the data center and inter-site networks that tie it together.
- Define how a new site goes live, then make it faster every time. Turn pod deployment into a repeatable, documented, largely automated playbook so that standing up the next 1 MW is a routine, not a project.
- Set and hit reliability targets. Own capacity planning, monitoring, alerting, on-call, incident response and post-incident reviews, and drive the GPU, node and network failure rates down over time.
- Hire, lead and develop a high-performance team of data center, systems and network engineers across time zones, and set the bar for how that team operates.
- Manage colocation, power, connectivity and hardware vendors and partners, including capacity contracts, lead times, spares and RMA flows, and site-level budgets.
- Partner with hardware and platform engineering so that what we design is operable at scale, and feed what you learn in the field back into the next generation of servers and pods.
- Use AI to accelerate the work: automate runbooks, triage and tooling with LLM-based agents, and expect your team to do the same, so that a small team can run a very large fleet.
- Own the operational side of security and compliance for our physical infrastructure: access control, hardening, asset tracking and audit readiness, including data residency requirements for in-country deployments.
Requirements
What we're looking for
- 15+ years in infrastructure engineering and operations, with at least several of those leading the function at a company that runs its own hardware at scale.
- A track record of building infrastructure operations across multiple sites, regions or countries, not just operating what someone else built. You've opened data centers, pods or colocation footprints and know what goes wrong between the plan and the first production workload.
- Deep, current, hands-on Linux expertise: kernel and driver behaviour, performance tuning, bare-metal provisioning, configuration management and fleet automation. You can still debug a misbehaving node at 2 a.m. and you'd rather fix the root cause than restart it.
- Strong storage fundamentals: NVMe, distributed and parallel file systems, object storage, and designing for the throughput and IOPS that model loading and caching demand.
- Strong networking fundamentals: L2/L3 design, BGP, data center fabrics, high-bandwidth interconnects and the debugging skills to find a flapping link or a misconfigured switch quickly.
- Experience running high-performance teams: you've hired, led and retained strong engineers, set clear standards, and built an on-call and incident culture that people are proud of rather than burned out by.
- You use AI to accelerate your own output every day, for scripting, runbooks, analysis, documentation and debugging, and you have a clear view of how a small team armed with these tools should operate differently from a traditional ops org.
- Comfortable owning outcomes in a fast-moving, ambiguous environment, making decisions with incomplete information and being accountable for uptime, cost and delivery.
- Able to travel to sites in the US and Europe as needed.
Nice to have
- 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 (InfiniBand or RoCE), 400G+ Ethernet and the tooling to monitor them.
- Experience with liquid-cooled, high-density or modular and containerized data center designs, and with power and cooling integration at the site level.
- Familiarity with the hardware supply chain: server ODMs, component lead times, inventory and spares strategy, and RMA economics.
- A strong automation stack background: 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.
Benefits
What we offer
- A rare mandate: build global AI infrastructure operations from the ground up, at a company whose business depends on doing it better than anyone else, with the budget and backing to match.
- Meaningful stock options.
- We're a remote-first collective, meeting in person twice a year to plan, brainstorm and celebrate wins. We keep core hours for collaboration and calls; outside those, you set your own schedule.
- Our release cycles are fast and intense, and they're followed by real downtime. We expect you to take it.
- Generous paid time off, including vacation, sick days and public holidays.
- Paid maternity, paternity and caregiver leave.
- Twice-yearly company retreats in inspiring locations.