---
title: 'Member of Technical Staff, Cluster Administration at Inferact'
canonical: 'https://feeny.ai/job/member-of-technical-staff-cluster-administration-inferact-san-francisco-hzy2pmhsv4ca'
type: 'job'
last_seen: '2026-09-08'
---

# Member of Technical Staff, Cluster Administration at Inferact

- **Company:** Inferact
- **Location:** San Francisco, CA
- **Compensation:** $200k–$400k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-08-21
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/inferact/595cbea0-7099-4416-a87c-efcc2876e654

## Job description

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 hands-on cluster administration engineer to own and operate the high-performance GPU compute infrastructure that keeps Inferact engineering productive. Inferact runs on expensive, high-performance GPU and HPC clusters across neo-cloud and dedicated compute providers. Your job is to make sure that infrastructure is healthy, available, observable, and usable around the clock.

You'll take ownership of cluster health, GPU availability, monitoring, alerting, scheduling, access, diagnostics, and incident response across the systems our engineers rely on every day. You'll work closely with engineering leadership and infrastructure owners to standardize how we provision, operate, debug, and scale compute across providers. Your work will directly impact how fast Inferact can build, test, and improve the systems powering vLLM.

## Skills and Qualifications

Minimum qualifications:

- Bachelor's degree or equivalent experience in computer science, engineering, systems administration, or similar.
- Hands-on experience administering large compute clusters, HPC environments, university or research clusters, supercomputing systems, or production GPU clusters.
- Strong Linux systems administration fundamentals across networking, processes, storage, package management, shell scripting, logs, access control, and system debugging.
- Experience operating GPU servers, including driver management, GPU health monitoring, node failures, memory errors, scheduler issues, and hardware diagnostics.
- Experience with cluster scheduling and resource allocation using SLURM, Kubernetes, or equivalent tooling.
- Ability to own urgent infrastructure incidents end-to-end when compute issues are blocking engineering teams.
- Ability to automate operational workflows using Bash, Python, Ansible, Terraform, Helm, or similar tooling.

Preferred qualifications:

- Experience operating GPU compute across providers such as Lambda, CoreWeave, Crusoe, Nebius, Together, Fireworks, RunPod, or similar environments.
- Experience improving cluster utilization, reducing idle or unavailable GPU capacity, and debugging scheduling or resource contention issues.
- Familiarity with high-performance GPU networking such as InfiniBand, RoCE, NVLink / NVSwitch, RDMA, NCCL, or equivalent systems.
- Experience with storage for HPC or ML workloads, including NFS, Lustre, Ceph, distributed filesystems, or other high-throughput storage systems.
- Experience managing secure access, identity, permissions, SSH, VPNs, bastion hosts, secrets, and basic infrastructure security hygiene.
- Background in research computing, scientific computing, ML infrastructure, SRE, platform engineering, or infrastructure operations for engineering-heavy teams.

Bonus points if you have:

- Managed GPU or HPC infrastructure in a university lab, national lab, research institution, AI infrastructure company, hedge fund, HFT firm, or large-scale ML platform team.
- Built monitoring, alerting, runbooks, health checks, or remediation workflows that materially reduced operational toil or incident resolution time.
- Operated Kubernetes clusters for ML or GPU workloads at meaningful scale.
- Standardized provisioning, diagnostics, monitoring, and operating patterns across multiple compute providers.
- Carried real operational responsibility for infrastructure used by many engineers or researchers.

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: Inferact 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)

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