ABOUT THE ROLE We're looking for an ML engineer who works across the full stack from model to silicon — comfortable optimizing training and inference performance on GPU/AI-accelerator infrastructure, building or tuning models, and adapting model and inference-engine design to the constraints of the underlying chip and its NPU. You'll move fluidly between algorithm work, systems-level software, and infrastructure work, closing the loop end-to-end rather than owning just one layer of the stack. This is a rare chance to work the full cycle of AI silicon, from model down to chip — something most ML engineers at large companies never get access to. WHAT YOU'LL DO • Optimize training and inference performance across GPU and AI-accelerator infrastructure, including MLOps pipelines • Design, train, and evaluate ML models (deep learning, LLM, CV, or recommendation systems) and take them into production • Work on at least one specialty area in depth — NPU hardening, systems-level software, inference engines, test/verification harnesses, or security-focused ML (see below) • Build or optimize inference engines and serving runtimes against real hardware constraints — latency, memory, and power • Collaborate closely with RTL/hardware, firmware, and QA teams to ship AI features end-to-end, from training through deployment and monitoring