Founding Engineer - Site Reliability

uRun • United States, CA

4d • $185,000 - $285,000 • Remote

About The Position

uRun is building the inference cloud for interactive AI, focusing on the compute layer that enables real-time, stateful inference at scale. As a founding Site Reliability Engineer, you will be instrumental in establishing the reliability culture from the ground up. This includes defining the observability stack, incident response playbooks, SLOs, and the on-call process. You will collaborate closely with infrastructure and platform engineers to ensure the stability and performance of the inference platform.

Requirements

• 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: you have built or owned monitoring stacks (Prometheus, Grafana, Datadog, or equivalent) and know what good alerting looks like.
• Proven incident response experience: you have led real incidents under pressure and written postmortems that actually changed behaviour.
• Hands-on with Kubernetes and cloud infrastructure (AWS preferred): you can debug a failing pod and a misconfigured VPC in the same afternoon.
• Strong software engineering fundamentals: you write automation, not just runbooks.
• Comfortable operating as the first and only SRE, setting standards without a template to follow.

Nice To Haves

• 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 neighbour problems, and billing-aware scheduling matter.
• Prior founding or sole SRE experience at an early-stage company.

Responsibilities

• 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.

Benefits

• Competitive salary and meaningful equity
• Health, dental, and vision — full coverage
• 401(k) — company-supported retirement savings
• FSA/HSA — flexible spending accounts for healthcare costs
• Paid time off — we trust you to manage your time
• Top-tier tooling — access to the best AI tools available: Claude, Codex, Kimi, and whatever else helps you move faster
• MacBook Pro and AirPods — the hardware you need, on us

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: No Education Listed