Member of Technical Staff, Site Reliability Engineer

Inferact; United States; Hybrid; USD 200,000 - 400,000; Full time

Job summary

Inferact is seeking a Site Reliability Engineer to make vLLM-powered inference systems reliable, observable, and production-ready at scale.

You will define SLOs, improve monitoring and alerting, strengthen incident response, drive post-mortems, and reduce operational risk before it reaches users. Based in San Francisco with US-wide remote flexibility for exceptional candidates, the role partners with engineering to ensure reliable AI inference systems at scale.

Qualifications

• Bachelor’s degree or equivalent experience in computer science, engineering, systems, infrastructure, or similar.
• Strong experience operating production systems with meaningful traffic, user impact, or infrastructure criticality.
• Deep understanding of SLOs, SLIs, error budgets, alerting, incident response, and post-mortem processes.
• Experience live-fighting major production incidents, including mitigation, root cause analysis, escalation, and follow-through on prevention work.
• Strong Linux, networking, systems debugging, observability, and distributed systems fundamentals.
• Ability to design operationally simple systems and identify likely failure modes before launch.
• Strong programming or scripting ability in Python, Go, Bash, or similar for automation, tooling, and reliability improvements.

Responsibilities

• Define SLOs, SLIs, and error budgets for production systems.
• Improve monitoring, alerting, and incident response processes.
• Drive post-mortems and implement prevention measures.
• Collaborate with engineering to improve service reliability.

Skills

SLOs/SLIs

Error budgets

Alerting

Incident response

Post-mortems

Linux

Networking

Observability

Distributed systems

Python/Go/Bash

Education

Bachelor’s degree or equivalent experience

Tools

Kubernetes

Docker

Terraform

Cloud infrastructure

Service meshes

CI/CD

Production deployment

Job description

Overview

Inferact’s mission is to grow vLLM as the world’s AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.

About the Role

We’re looking for a Site Reliability Engineer to help make vLLM-powered inference systems reliable, observable, and operationally simple at production scale. This role is for someone who thinks about failure before launch, designs systems that are easier to operate, and knows how to turn incidents into durable improvements rather than one-off fixes.

You’ll work across engineering and infrastructure to define SLOs, improve monitoring and alerting, strengthen incident response, drive post-mortems, and reduce operational risk before it reaches users. Your work will directly impact the reliability, availability, and production readiness of the systems powering AI inference at scale.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, engineering, systems, infrastructure, or similar.
• Strong experience operating production systems with meaningful traffic, user impact, or infrastructure criticality.
• Deep understanding of SLOs, SLIs, error budgets, alerting, incident response, and post-mortem processes.
• Experience live-fighting major production incidents, including mitigation, root cause analysis, escalation, and follow-through on prevention work.
• Strong Linux, networking, systems debugging, observability, and distributed systems fundamentals.
• Ability to design operationally simple systems and identify likely failure modes before launch.
• Strong programming or scripting ability in Python, Go, Bash, or similar for automation, tooling, and reliability improvements.

Preferred qualifications:

• Experience supporting ML infrastructure, inference systems, GPU workloads, Kubernetes-based platforms, or high-scale backend services.
• Experience building or improving observability systems using metrics, logs, traces, dashboards, alerts, and runbooks.
• Experience with Kubernetes, Docker, Terraform, cloud infrastructure, service meshes, CI/CD systems, or production deployment platforms.
• Experience driving incident review culture, post-mortem processes, reliability reviews, and prevention-oriented engineering work.
• Ability to partner with engineering teams to improve service design, release safety, capacity planning, and operational readiness.

Bonus points if you have:

• Owned reliability for high-throughput, latency-sensitive, or mission-critical production systems.
• Supported AI inference, model serving, GPU clusters, ML platforms, or distributed serving infrastructure.
• Built automation that reduced toil, improved recovery time, or prevented repeat incidents.
• Led incident response for severe outages with clear communication across engineering and leadership.
• Created practical SLOs, dashboards, alerts, runbooks, or release gates that improved production reliability.

Logistics

• Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.
• Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
• Visa sponsorship: We sponsor visas on a case‑by‑case basis.
• Benefits: We offers generous health, dental, and vision benefits as well as 401(k) company match.