[Hiring] Member of Technical Staff, Developer Relations @Inferact

Role 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 an 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.

• 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
• Model-server tradeoffs across vLLM and adjacent systems

Your work will shape how the broader AI infrastructure community learns, adopts, and builds with vLLM.

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.

Requirements

• 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.

Benefits

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