Founding Engineer - Site Reliability

The Problem We Saw

Most AI infrastructure is built for batch: send a query, wait, get a response, reset. Powerful, but transactional. AI is becoming interactive — sessions that hold state, models that stay alive between turns, generation that responds as it runs — and the infrastructure to deliver that at scale doesn't really exist yet.

The bottleneck isn't the models anymore. It's the infrastructure underneath them.

What We're Building to Fix It

uRun is the inference cloud for interactive AI: the compute layer that makes real-time, stateful inference possible at scale. We came out of stealth in April 2026, are backed by top-tier investors, and are founded by Keegan McCallum, who scaled inference infrastructure for some of the most demanding generative AI workloads in production.

We're an infrastructure company. We build the layer that model labs, builders, and research teams ship on top of.

Where You Come In

Reliability at uRun isn't a feature — it's the product. When model labs and production teams build on top of our inference platform, they are trusting us with their uptime, their latency, and their users. As our Site Reliability Engineer, you will own that trust end-to-end.

This is a founding SRE hire. You will define the reliability culture from scratch: the observability stack, the incident response playbooks, the SLOs, and the on-call process. You will work directly with infrastructure and platform engineers to close the gap between what we ship and what stays up.

What You'll Actually Be Doing Day-to-Day

• Define and own SLOs and error budgets across uRun's inference platform and supporting infrastructure
• Build and maintain the observability stack end-to-end: metrics, logging, tracing, and alerting across a distributed GPU compute environment
• Lead incident response: detection, triage, resolution, and blameless postmortems that drive lasting fixes
• Partner with ML infrastructure engineers to embed reliability into the deployment pipeline from day one
• Design and maintain runbooks, on-call rotations, and escalation paths as the team scales
• Drive capacity planning and traffic management across heterogeneous compute to protect latency and availability under load
• Identify and eliminate toil through automation, building systems that scale without scaling the team proportionally

What Skills You Need for the Journey

• 7+ years in site reliability, production engineering, or infrastructure engineering in a high-availability, low-latency environment
• Deep experience owning SLOs, error budgets, and on-call processes in production at scale
• Strong observability background: experience building or owning monitoring stacks (Prometheus, Grafana, Datadog, or equivalent) and knowing what good alerting looks like
• Proven incident response experience: leading real incidents under pressure and writing actionable postmortems
• Hands-on experience with Kubernetes and cloud infrastructure (AWS preferred), including debugging pod and networking issues
• Strong software engineering fundamentals focused on writing automation
• Comfort operating as the first and only SRE, setting standards without existing templates

Things That Will Give You an Edge

• Experience supporting GPU compute or ML inference infrastructure in production
• Familiarity with stateful workloads, long-running sessions, or streaming inference systems
• Exposure to multi-tenant platforms where isolation, noisy neighbor problems, and billing-aware scheduling matter
• Prior founding or sole SRE experience at an early-stage company

What You'll Get in Return

• Competitive salary and meaningful equity in an early-stage AI infrastructure company
• Health, dental, and vision (full coverage)
• 401(k) company-supported retirement savings
• FSA/HSA flexible spending accounts
• Paid time off
• Access to top-tier AI tooling (Claude, Codex, Kimi, etc.)
• Hardware provided (MacBook Pro and AirPods)

How We Work

We're an infrastructure and developer-tools company focused on high ownership, a small team, and a high bar. You will define systems from scratch in an environment with shifting priorities and deliberate ambiguity.