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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Developer Relations Engineer - **Company:** Inferact, Inc. - **Location:** San Francisco, CA, United States - **Salary:** $200,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Compilers, Nvidia CUDA, Programming Tools, Distributed Systems, Machine Learning, Open Source Technology, AI Infrastructure, Graphics Processing Unit (GPU), High Performance Computing, Pytorch, Large Language Models, Information Technology, Machine Learning Operations, TensorRT - **Published:** September 4, 2026 - **Apply:** https://startup.jobs/member-of-technical-staff-developer-relations-inferact-8280045 ## About the Role * 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. ## Description 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., * 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. ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Just-in-time Compilation in JVM](https://www.wearedevelopers.com/videos/240-just-in-time-compilation-in-jvm) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) ## Related Articles - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Dev Digest 132 - Binging WADFlix?](https://www.wearedevelopers.com/magazine/473-dev-digest-132-binging-wadflix) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)