← Search all jobs

Member of Technical Staff, Developer Relations

Inferact

San Francisco, CA · $200k–$400k a year · Posted June 17, 2026

Just laid off? Start here: a plan for the first weeks

See every open role at Inferact

More in California job market · Member of Technical Staff jobs in San Francisco, CA (182) · All Member of Technical Staff jobs (368) · Technology & Software · Engineering jobs

More roles at Inferact

Similar jobs

Where San Francisco, CA ranks

Where San Francisco, CA placed in our monthly rankings, month by month. Each month stays online at its own address.

Pay compared with the market

This listing pays about $300,000 a year. Across 136 open member of technical staff listings that state pay, the middle half runs $212,000 to $270,000 and the median is $240,000. That is 25% above the median, above the middle half of those listings.

Listed pay is what employers advertise, not what people are paid.

Member of Technical Staff salaries by state · Check your own pay

How AI is used in this kind of work

Among software developers, AI is already used for some of the tasks. Measured exposure for software developers: 29% (some).

Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.

Check how this applies to your own job

Skills this job asks for

Say which you have and which you want to build. We use it to match you to jobs like this one.

About the job

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.

Inferact, Inc. is an E-Verify company, and you may review the Notice of E-Verify Participation https://drive.google.com/file/d/1TWSDK2aZycxydMvPle230QSwuQtuNIyM/view?usp=sharing and the Right to Work posters in English and Spanish https://drive.google.com/file/d/1WxUM6hcXhb3CuJ_nWgnMmHF99XsuT9sI/view?usp=sharing.