---
title: 'Member of Technical Staff, Vulnerability Management at Inferact'
canonical: 'https://feeny.ai/job/member-of-technical-staff-vulnerability-management-inferact-san-francisco-28n91fzms5pv'
type: 'job'
last_seen: '2026-10-13'
---

# Member of Technical Staff, Vulnerability Management at Inferact

- **Company:** Inferact
- **Location:** San Francisco, CA
- **Compensation:** $200k–$400k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-10-02
- **Last confirmed live:** 2026-10-13
- **Apply:** https://jobs.ashbyhq.com/inferact/2db45f74-bed2-4cd6-aa0a-1f67c44302dd

## 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 Member of Technical Staff, Vulnerability Management to own Inferact's side of vLLM's vulnerability-management process and help keep a critical piece of AI infrastructure secure and production-grade. You'll develop a deep understanding of vLLM's architecture, independently investigate vulnerability reports, and turn technical findings into clear, actionable work for the core team. Working primarily in open source, you'll collaborate with vLLM maintainers, Red Hat counterparts, security firms, and researchers working with frontier AI models. You'll drive reports from initial triage through analysis and resolution, helping coordinate fixes, security advisories, and releases under the project's established process. You'll also identify practical security best practices that strengthen vLLM as it evolves. This is a hands-on product security role that combines technical investigation, sound judgment, and ownership of follow-through. [vLLM's vulnerability-management process](https://docs.vllm.ai/en/latest/contributing/vulnerability_management/)

## Skills and Qualifications

Minimum qualifications:

- Hands-on product security experience, with a strong orientation toward infrastructure software and the security of complex software systems.
- Ability to read source code, debug unfamiliar systems, reproduce reported issues, and explain root cause and practical security impact rather than simply forward findings.
- Strong systems reasoning, including the ability to understand architecture, trust boundaries, deployment assumptions, and how different software components interact.
- Ability to learn vLLM's core architecture and independently manage vulnerability investigations while bringing in maintainers where their expertise is needed.
- Sound prioritization and risk-assessment judgment, with clear documentation of evidence, affected behavior, remediation needs, and next steps.
- Strong written and verbal communication, discretion with sensitive reports, and the ability to work constructively with engineers, researchers, and external security collaborators.

Preferred qualifications:

- Experience with vulnerability management and security best practices for open-source infrastructure software.
- Experience coordinating vulnerability resolution across reporters, maintainers, security teams, and software releases.
- Familiarity with vLLM, inference engines, ML infrastructure, or similarly complex distributed software; prior vLLM contributions are helpful but not required.
- Experience preparing security advisories, assessing affected versions, and working with CVE and coordinated-disclosure workflows.
- Experience translating security findings into practical architecture reviews, regression tests, secure development practices, or deployment guidance.

Bonus points if you have:

- Helped resolve vulnerabilities in an open-source infrastructure project and can explain your technical contribution and coordination with maintainers.
- Built security tooling or automation that improved investigation quality or reduced repetitive triage work.
- Turned recurring vulnerability patterns into maintainable fixes, tests, or documentation that helped prevent similar issues.

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: Applicable benefits will be confirmed based on the final role location.

## 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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- [GTM Engineer](https://feeny.ai/job/gtm-engineer-inferact-san-francisco-4kw95nqkxq2p) — San Francisco, CA
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