Staff ML Engineer

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

• Harden and extend NPU cores (e.g. building on an open RVV/tensor core like CoralNPU) into production silicon

• Build or optimize inference engines and serving runtimes against real hardware constraints — latency, memory, and power

• Work below the application layer where needed — BMC firmware, embedded Linux, or RTOS (e.g. Zephyr) — so AI features run reliably on real systems

• Build automated test/verification harnesses that close the loop for AI-assisted RTL/DV, hardware bring-up, or manufacturing test

• Apply ML to security — AI-driven log/intrusion analysis, AI-assisted penetration testing, or firmware/hardware security work

• Collaborate closely with RTL/hardware, firmware, and QA teams to ship AI features end-to-end, from training through deployment and monitoring