---
title: 'Member of Technical Staff, Site Reliability Engineer at Inferact'
canonical: 'https://feeny.ai/job/member-of-technical-staff-site-reliability-engineer-inferact-san-francisco-4hdd72zebn0b'
type: 'job'
last_seen: '2026-09-08'
---

# Member of Technical Staff, Site Reliability Engineer at Inferact

- **Company:** Inferact
- **Location:** San Francisco, CA
- **Compensation:** $200k–$400k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-08-20
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/inferact/ad992ead-2a9a-4694-8fca-0504354548cd

## 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.

## About Inferact

## Company Overview
- **One-liner**: Inferact is a startup founded by the creators of vLLM, the leading open-source LLM inference engine, dedicated to making AI inference cheaper and faster at global scale.
- **Entity Type**: Private (Seed stage; raised $150M in seed funding)
- **Headquarters**: San Francisco, California, United States (with a second office in Singapore)
- **Founded**: 2025
- **Founders**: Simon Mo (CEO), Woosuk Kwon, Kaichao You (Chief Scientist), Roger Wang, Joseph Gonzalez, Ion Stoica

## Core Business
- **Primary industry**: AI infrastructure / open-source inference engine for large language models
- **Target customers**: AI labs, hyperscalers, startups, and enterprises deploying large-scale AI models (B2B, primarily technical teams)
- **Mission**: Grow vLLM as the world’s AI inference engine and accelerate AI progress by making inference cheaper and faster.

## Products & Services
- **vLLM (Open-Source Inference Engine)**: The core product – an open-source LLM inference engine that supports 500+ model architectures and runs on 200+ accelerator types. Inferact stewards and supercharges vLLM, with all optimizations flowing back to the community.
- **Managed Inference Infrastructure (in development)**: Inferact is building infrastructure to absorb the complexity of deploying frontier models at scale, aiming to make it as simple as spinning up a serverless database.

## Market Standing
- **Valuation/Market Cap**: Not disclosed (private company)
- **Key Metric**: Total funding of $150M (seed round, announced 2026)
- **Notable Investors/Partners**: Lightspeed Venture Partners (lead), Redpoint Ventures, Andreessen Horowitz, Altimeter Capital, Sequoia Capital, The House Fund, GC&H Investments, and others. Partnerships include NVIDIA, Red Hat, DigitalOcean, and Cohere.
- **Growth Signals**: 
  - $150M seed round – one of the largest seed rounds in AI infrastructure.
  - 22 employees with +27.3% monthly headcount growth.
  - vLLM ecosystem: 2,000+ contributors, 500+ model architectures, 200+ accelerator types.
  - Day-zero support for new model architectures (e.g., Cohere’s Command A+) and hardware integrations.
  - Active hiring with 5 open positions across inference, performance, kernel engineering, and cloud orchestration.

## Competitive Advantages
- **Deep ecosystem moat**: vLLM is the de facto standard open-source inference engine, with a massive community and integrations across models and hardware that took years to build.
- **Founding team credibility**: Creators and core maintainers of vLLM, with experience deploying at frontier scale (research and production).
- **Hardware-software co-optimization**: Positioned at the intersection of model innovation and hardware diversity, enabling day-zero compatibility and performance optimizations.
- **Open-source commitment**: All improvements flow back to vLLM, ensuring community trust and rapid adoption.

## Strategic Focus
- **Current priorities**: Push vLLM performance further, deepen support for emerging model architectures (MoE, multimodal, agentic), expand hardware coverage (200+ accelerators), and build managed infrastructure to simplify deployment.
- **Growth direction**: Close the capability gap between models and serving systems; absorb complexity so teams can focus on innovation rather than infrastructure.

## Why Work Here
- **Culture**: High-caliber engineering team with roots in vLLM, PyTorch, and top AI labs. Emphasis on open-source contribution and cutting-edge inference research.
- **Work policy**: Hybrid with a San Francisco HQ; at least one open role (Member of Technical Staff, Exceptional Generalist) is listed as Remote.
- **Notable perks**: Opportunity to work at the frontier of AI inference, directly impact the open-source ecosystem, and collaborate with partners like NVIDIA, Red Hat, and major AI labs.
- **Engineering culture**: Strong focus on systems engineering, kernel optimization, and cloud orchestration – ideal for engineers passionate about performance and infrastructure.

## Sources
1. [inferact.ai](https://inferact.ai/)
2. [LinkedIn](https://www.linkedin.com/company/inferact)
3. [CB Insights](https://www.cbinsights.com/company/inferact)
4. [Sequoia Capital](https://sequoiacap.com/companies/inferact/)
5. [Ashby Jobs](https://jobs.ashbyhq.com/inferact)

## Other roles at Inferact

- [Member of Technical Staff, Inference](https://feeny.ai/job/member-of-technical-staff-inference-inferact-remote-ezb6x9bq2fty)
- [Member of Technical Staff, Cloud Orchestration (Remote)](https://feeny.ai/job/member-of-technical-staff-cloud-orchestration-remote-inferact-remote-pzza0bak1bw7)
- [Member of Technical Staff, Cluster Administration](https://feeny.ai/job/member-of-technical-staff-cluster-administration-inferact-san-francisco-hzy2pmhsv4ca) — San Francisco, CA
- [Founding Product Designer](https://feeny.ai/job/founding-product-designer-inferact-san-francisco-8fhyc666jrb6) — San Francisco, CA
- [Head of Engineering](https://feeny.ai/job/head-of-engineering-inferact-san-francisco-rhcv5bpns37j) — San Francisco, CA
- [Product Marketing Manager](https://feeny.ai/job/product-marketing-manager-inferact-san-francisco-ap07axndjdjb) — San Francisco, CA
- [Member of Technical Staff, AMD GPU Performance Engineering](https://feeny.ai/job/member-of-technical-staff-amd-gpu-performance-engineering-inferact-san-francisco-q425hfnsdrh2) — San Francisco, CA
- [Member of Technical Staff, TPU Performance Engineering](https://feeny.ai/job/member-of-technical-staff-tpu-performance-engineering-inferact-singapore-hsapg2c1ks2d) — Singapore
- [Member of Technical Staff, AMD GPU Performance Engineering](https://feeny.ai/job/member-of-technical-staff-amd-gpu-performance-engineering-inferact-singapore-q7jn24nd0rbf) — Singapore
- [Member of Technical Staff, Performance and Scale](https://feeny.ai/job/member-of-technical-staff-performance-and-scale-inferact-singapore-vyavkymh08h3) — Singapore