Required Qualifications

• Education: M.S. or higher in Electrical Engineering, Computer Science, or a related field with a focus on AI/ML or VLSI.
• Professional Experience: 7+ years of experience in the semiconductor or EDA industry, with a proven track record of deploying AI/ML models in a production capacity.
• Broad EDA Knowledge: Hands-on experience across the full silicon lifecycle (RTL-to-GDS), with specific exposure to Physical Design, Place-and-Route, and Physical Verification (DRC/LVS).
• Software Proficiency: Strong programming skills in Python, C++, and SystemVerilog, along with experience in scripting (Tcl, Shell).
• Machine Learning Stack: Proficiency in PyTorch or TensorFlow, and experience with state-of-the-art framework orchestration, custom inference optimization tools, and model evaluation harnesses.

Preferred Qualifications & Skills

• Advanced Education: Ph.D. with research specifically focused on AI/ML for EDA and metric modeling.
• Practical Chip Design: Extensive experience with advanced technology nodes (7nm, 5nm, 3nm, or below) and physical verification toolsets (e.g., IC Validator, Calibre).
• Specialized ML: Demonstrated experience in Multi-objective Optimization, Transfer Learning, and the application of machine learning architectures to hardware problems.
• Systems Engineering: Deep expertise in operationalizing GenAI platforms on distributed, multi-GPU cloud environments.
• Hardware Acceleration: Background in optimizing algorithms for FPGA or SoC deployment and hardware-efficient ML implementation.