About the role
structured by ORIOverview 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.
What you will do
- 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 a
- Shape how the broader AI infrastructure community learns, adopts, and builds with vLLM.
What they are looking for
- 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.
Nice to have
- 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.
- 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.
Benefits
- generous health, dental, and vision benefits
- 401(k) company match
Full posting text
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.