• 3–5+ years of hands-on AI/ML experience; Bachelor’s required, Master’s 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
• Hands-on experience in at least 1 of the following 5 specialty areas:
– 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