---
title: 'Member of Technical Staff, Developer Relations at Inferact'
canonical: 'https://feeny.ai/job/member-of-technical-staff-developer-relations-inferact-san-francisco-bbdh5zkbavj5'
type: 'job'
last_seen: '2026-09-08'
---

# Member of Technical Staff, Developer Relations at Inferact

- **Company:** Inferact
- **Location:** San Francisco, CA
- **Compensation:** $200k–$400k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-06-17
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/inferact/e1a91db5-1cd4-4688-863b-33ab88b40a4d

## 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 Developer Relations Engineer to help make vLLM the default way developers understand, build, and scale AI inference. This is not a generic DevRel role. We're looking for a inference systems educator-builder: someone who can understand vLLM as a deep LLM inference systems project, teach hard technical concepts clearly, and create public artifacts that help practitioners build better systems.

You'll write technical deep dives, build demos, create tutorials, contribute to docs and examples, host workshops, and help developers understand topics like KV cache, continuous batching, prefix caching, prefill and decode, quantization, GPU serving, latency versus throughput, and model-server tradeoffs across vLLM and adjacent systems. Your work will shape how the broader AI infrastructure community learns, adopts, and builds with vLLM.

## Skills and Qualifications

Minimum qualifications:

- Bachelor's degree or equivalent experience in computer science, engineering, machine learning, systems, or similar.
- Strong technical understanding of LLM inference systems, model serving, GPU inference, distributed runtimes, scheduling, batching, quantization, or related infrastructure.
- Ability to credibly explain systems concepts such as KV cache, PagedAttention, continuous batching, prefill / decode scheduling, prefix caching, speculative decoding, tensor parallelism, data parallelism, or latency versus throughput tradeoffs.
- Experience with vLLM or adjacent inference technologies such as SGLang, TensorRT-LLM, TGI, LoRAX, Ray Serve, FlashInfer, BentoML, Baseten-style serving platforms, or similar systems.
- A strong public portfolio of technical artifacts, such as blogs, tutorials, workshops, courses, OSS docs, benchmark posts, architecture explainers, conference talks, demos, or runnable repositories.
- Ability to write and teach for practitioners without sounding like a content marketer.
- Strong engineering judgment, product taste, and ability to turn raw technical material into useful developer education.

Preferred qualifications:

- Prior work in ML systems, distributed systems, HPC, compilers, GPU kernels, serving infrastructure, MLOps, developer tooling, or open-source infrastructure.
- Experience creating technical content that teaches reusable mental models, not just product features.
- Experience contributing to developer-facing open source through docs, tutorials, examples, cookbooks, demos, or community support.
- Existing credibility or community presence in AI infrastructure, OSS, CUDA / GPU, Ray, vLLM, PyTorch, Modal, BentoML, Baseten, Predibase, Together AI, Anyscale, LMSYS, or similar ecosystems.
- Ability to host workshops, create hands-on labs, present technical talks, and help developers move from concept to working code.

Bonus points if you have:

- Written widely-shared technical blogs, courses, or architecture deep dives on LLM inference, model serving, GPU serving, or ML systems.
- Built demos, benchmarks, tutorials, or repositories around vLLM, SGLang, TensorRT-LLM, TGI, Ray Serve, FlashInfer, or related systems.
- Contributed to open-source ML infrastructure, inference systems, developer tooling, or technical education projects.
- Created practitioner-facing content with code, diagrams, benchmarks, demos, or end-to-end labs.
- Built a durable personal portfolio that demonstrates technical depth, taste, and a strong point of view.

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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