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HomeCompaniesInferactMember of Technical Staff, Developer Relations

Member of Technical Staff, Developer Relations

Inferact · San Francisco · On Site · Active · Ashby

Job facts

FieldValue
CompanyInferact
TitleMember of Technical Staff, Developer Relations
Normalized title-
Department / teamResearch & Engineering / Research & Engineering
LocationSan Francisco, CA, United States
Work modelOn Site
Employment typeFull Time
Salary-
Statusactive
ATS providerAshby
Posted / first seen / 2026-06-18
Changed / last seen2026-06-18 / 2026-06-19

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PageWhat it containsOpen
Company jobsActive postings from Inferact.Open
Company breakdownsRole, location, ATS, and work model facets for this company.Open
ATS provider jobsActive postings observed through Ashby.Open
Provider filtered searchThe same provider as a filtered job collection.Open
City jobsActive postings in San Francisco.Open
Department jobsActive postings in Research & Engineering.Open
Work model jobsActive On Site postings.Open
Lifecycle eventsOpen, update, close, and reopen events for this posting.Open
Original postingCanonical source or apply URL captured from the ATS.Open

Linked records

CompanyInferact
Sourcee696b120-5356-4b40-9538-3bab05dde7af
ATS providerAshby

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.

Full job record

Job ID5eb8ea1b20672a7ca71602a06cc7bfe48b9fa4b0
Org ID15d4b3b1-5ecf-489b-8865-7cbdc8aa3c15
Source IDe696b120-5356-4b40-9538-3bab05dde7af
Board IDe696b120-5356-4b40-9538-3bab05dde7af
Providerashby
Provider Job Keye1a91db5-1cd4-4688-863b-33ab88b40a4d
TitleMember of Technical Staff, Developer Relations
Normalized Title
Statusactive
Activeyes
Location TextSan Francisco
DepartmentResearch & Engineering
TeamResearch & Engineering
Employment Typefull_time
Workplace Typeon_site
Remote Policy
CountryUnited States
RegionCA
CitySan Francisco
Salary Raw
Salary Min
Salary Max
Salary Currency
Salary Period
Source URLhttps://jobs.ashbyhq.com/Inferact/e1a91db5-1cd4-4688-863b-33ab88b40a4d
Apply URLhttps://jobs.ashbyhq.com/Inferact/e1a91db5-1cd4-4688-863b-33ab88b40a4d/application
First Seen At2026-06-18 10:28:24Z
Last Seen At2026-06-19 09:44:34Z
Last Checked At2026-06-19 09:44:34Z
Last Changed At2026-06-18 10:28:24Z
Inactive At
Source Posted At
Source Updated At
Raw Payload Uris3://job-postings-prod-raw-590183727216/raw/provider=ashby/board=Inferact/date=2026-06-19/2026-06-19T09-44-33-900Z-0f08283e84905e180b6897544ad0b22a758c78896b5fb177211904a8fe461233.json
Event Fields
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Parsed Structured
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Extensions
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Native Structured
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}
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