Works on the core vLLM inference engine to optimize model execution for LLM and diffusion model serving across diverse hardware. Implements model architectures and inference techniques from research papers, including mixture-of-experts and multimodal models. Stack includes Python, PyTorch internals, transformer architectures, and vLLM.
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:
