Inferact · San Francisco
Inference runtime engineer role working on the core of vLLM to optimize how LLM and diffusion models execute across diverse hardware and architectures, including mixture-of-experts and multimodal models. Requires Python, PyTorch internals, and experience with inference engines such as vLLM, TensorRT-LLM, SGLang, or TGI, plus implementing techniques from research papers.
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.
We're looking for an inference runtime engineer to push the boundaries of what's possible in LLM and diffusion model serving. Models grow larger. Architectures shift: mixture-of-experts, multimodal, agentic. Every breakthrough demands innovations on the inference engine itself. You'll work at the core of vLLM, optimizing how models execute across diverse hardware and architectures. Your work will directly impact how the world runs AI inference.
Minimum qualifications:
Preferred qualifications:
Bonus points if you have:
Inferact, Inc. is an E-Verify company, and you may review the Notice of E-Verify Participation and the Right to Work posters in English and Spanish.
Compensation: $200K - $400K