Member of Technical Staff, Developer Relations

The role

This person will act as an inference systems educator-builder to help developers understand, build, and scale AI inference with vLLM. They will write technical deep dives, build demos, create tutorials, contribute to documentation, and host workshops.

What you will do

• Write technical deep dives
• Build demos
• Create tutorials
• Contribute to docs and examples
• Host workshops
• Help developers understand topics like KV cache, continuous batching, prefix caching, prefill and decode, quantization, GPU serving, latency versus throughput, and model-server tradeoffs

Required 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 parallel
• 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
• 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

Benefits

• Health benefits
• Dental benefits
• Vision benefits
• 401(k) company match

Before you apply

• We sponsor visas on a case-by-case basis

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, they sit at the intersection of models and hardware.