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About the Role

We are seeking a Developer Relations Engineer to establish vLLM as the preferred method for developers to understand, build, and scale AI inference. This role requires an individual who can deeply comprehend vLLM as a complex LLM inference systems project, articulate challenging technical concepts clearly, and produce public resources that empower practitioners to develop superior systems.

Responsibilities

• Write technical deep dives.
• Build demos.
• Create tutorials.
• Contribute to documentation and examples.
• Host workshops.
• Educate developers on topics including KV cache, continuous batching, prefix caching, prefill and decode, quantization, GPU serving, latency versus throughput, and model-server tradeoffs across vLLM and adjacent systems.
• Shape the AI infrastructure community's learning, adoption, and building practices with vLLM.

Requirements

• Bachelor's degree or equivalent experience in computer science, engineering, machine learning, systems, or a related field.
• Strong technical understanding of LLM inference systems, model serving, GPU inference, distributed runtimes, scheduling, batching, quantization, or related infrastructure.
• Ability to clearly 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 similar inference technologies (e.g., SGLang, TensorRT-LLM, TGI, LoRAX, Ray Serve, FlashInfer, BentoML, Baseten-style serving platforms).
• A strong public portfolio showcasing technical artifacts like blogs, tutorials, workshops, courses, OSS documentation, benchmark posts, architecture explainers, conference talks, demos, or runnable repositories.
• Ability to write and teach for practitioners in a non-marketing style.
• Strong engineering judgment, product taste, and the ability to transform raw technical material into valuable developer education.
• Prior experience 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.
• Experience contributing to developer-facing open source projects.
• 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 guide developers from concept to working code.
• Experience writing widely-shared technical blogs, courses, or architecture deep dives on LLM inference, model serving, GPU serving, or ML systems.
• Experience building demos, benchmarks, tutorials, or repositories around vLLM, SGLang, TensorRT-LLM, TGI, Ray Serve, FlashInfer, or related systems.
• Experience contributing to open-source ML infrastructure, inference systems, developer tooling, or technical education projects.
• Experience creating practitioner-facing content with code, diagrams, benchmarks, demos, or end-to-end labs.
• Experience building a durable personal portfolio demonstrating technical depth, taste, and a strong point of view.

Skills

• LLM inference systems
• Model serving
• GPU inference
• Distributed runtimes
• Scheduling
• Batching
• Quantization
• KV cache
• PagedAttention
• Continuous batching
• Prefill / decode scheduling
• Prefix caching
• Speculative decoding
• Tensor parallelism
• Data parallelism
• Latency versus throughput tradeoffs
• vLLM
• SGLang
• TensorRT-LLM
• TGI
• LoRAX
• Ray Serve
• FlashInfer
• BentoML
• Baseten-style serving platforms
• ML systems
• Distributed systems
• HPC
• Compilers
• GPU kernels
• Serving infrastructure
• MLOps
• Developer tooling
• Open-source infrastructure
• CUDA / GPU
• Ray
• PyTorch
• Modal
• Predibase
• Together AI
• Anyscale
• LMSYS

Location

• San Francisco, California
• Remote in US

Work Type

• Remote
• Onsite

Experience Level

• Mid-level
• Senior

Education Level

• Bachelor's degree or equivalent experience in computer science, engineering, machine learning, systems, or similar.

Salary/Compensations

• $200,000 - $400,000 USD + equity

Benefits

• Generous health, dental, and vision benefits
• 401(k) company match

About the Company

• 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.