Staff ML Engineer

Role: Staff ML Engineer Location: San Jose, CA, USA Department: ML AI-enhanced security processor company redefining the control and management of every digital system.The company builds silicon-rooted security and management chips — including the TCU (Trusted Control/Compute Unit) — for AI data center infrastructure, combining platform security, BMC/firmware, and on-chip AI for real-time threat detection and dynamic power/thermal management.About the roleWe'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 monitoringQualifications What we're looking for ● 5–7+ years of hands-on AI/ML experience; Master's required, PhD preferred● Hands-on experience with AI/ML infrastructure and performance — GPU clusters, distributed training, inference-serving optimization, MLOps pipelines● Model / algorithm development experience — designing, training, and evaluating ML models● Experience taking models into production — feature engineering, data pipelines, deployment● AI chip / hardware-aware ML experience — optimizing inference engines for a specific chip, or adapting model architecture/quantization to chip constraints● Deep, hands-on expertise in at least 2 of the following 5 specialty areas — we don't expect all five:– NPU / AI-accelerator — hardening or extending an NPU core into production silicon, mapping models onto MAC/tensor-engine constraints, or NPU-aware RTL/DV work– Systems / Sys-level software — BMC firmware, embedded Linux, RTOS (e.g. Zephyr), or other low-level system software– Inference engine / runtime — built or materially optimized an inference engine or serving runtime against real hardware constraints– Test / verification harness — built an automated harness that closes a loop, e.g. an agent-driven RTL/DV test runner or a hardware bring-up / MFG test harness– Cyber security — AI-driven log/intrusion analysis, AI-assisted penetration testing, or firmware/hardware security