Identify and prioritize opportunities to apply machine learning, data analytics, and automation to engineering workflows (e.g., predictive coverage modeling, test/testbench generation, timing and congestion prediction, floorplan and place-and-route optimization, RTL code assistance, anomaly detection in regression and bring-up data, root-cause and failure triage). Solid understanding of core development flows and tooling (e.g., simulation, regression, coverage collection and debug for verification; RTL/synthesis flows for design; timing, place-and-route, and physical verification for physical design), sufficient to credibly scope and prioritize AI initiatives across these domains.