AI-Driven Physical Design Engineering (PhD Intern)

Research and implement AI/ML methods, intelligent workflows, and automation systems to improve physical design execution and decision-making for high-performance silicon. Develop graph-based and data-analysis capabilities, collaborate with AI and design teams, and benchmark results against traditional methods using power, performance, and area metrics.

Responsibilities

• Research and implement AI/ML methods, intelligent workflows, and automation systems to improve physical design execution and decision-making for high-performance silicon. Develop graph-based and data-analysis capabilities, collaborate with AI and design teams, and benchmark results against traditional methods using power, performance, and area metrics.

Requirements

• Applicants must be enrolled in a PhD program in Electrical Engineering, Computer Engineering, Computer Science, or a related field, and have familiarity with physical design tools, scripting, and digital design concepts. Preferred qualifications include strong Python and ML experience, knowledge of graph neural networks or reinforcement learning, data-analysis tools, and physical design fundamentals.

Benefits

• Competitive Pay
• Stock Bonuses
• Health Benefits
• Retirement Benefits
• Vacation

Key Skills