We are looking for a highly experienced, hands-on ML Systems Architect to define the next generation of large language model (LLM) inference architecture. This role sits at the intersection of hardware and software: you will reason about the entire pipeline-from a prompt (or batch of prompts) arriving at the system to the generation of each output token-and use that understanding to guide architecture decisions across compute, memory, and interconnect. You will be a key voice in shaping what the next macro-architecture for inference should look like, anticipating how emerging model trends (e.g., mixture-of-experts, longer context windows, new attention mechanisms) will reshape system requirements.