Lead Software Engineer, ML

Position Overview

The Cadence Digital and Signoff Group Machine Learning team is a high-energy engineering team focused on exploring and implementing deep machine learning techniques and agentic frameworks for Electronic Design Automation (EDA) tools. The team works at the intersection of advanced AI research, optimization, and production-grade software development to help solve complex challenges in digital design and signoff.

Primary Responsibilities

• Design and implement gradient-based and gradient-free optimization algorithms for complex EDA and system design challenges.
• Research, prototype, and develop machine learning and deep learning approaches that improve EDA tool capabilities, performance, and usability.
• Design, implement, verify, and maintain high-quality software for digital design and signoff markets.
• Develop APIs and software components that integrate machine learning models, agentic AI workflows, and optimization techniques into production tools.
• Work in Linux-based development environments using Python, C/C++, and modern ML/DL frameworks such as PyTorch.
• Collaborate with worldwide cross-functional teams across research, engineering, product, and customer-facing organizations.
• Translate deep learning research concepts into scalable, maintainable software solutions for real-world engineering workflows.

Required Qualifications

• Strong software engineering background with hands-on experience designing, developing, verifying, and maintaining production-quality software.
• Proficiency in Python and C/C++ development.
• Experience working in Linux-based development environments.
• Solid understanding of machine learning and deep learning algorithms.
• Hands-on experience with ML/DL frameworks such as PyTorch.
• Experience or strong interest in agentic AI, LLM-based workflows, and agentic frameworks.
• Able to design and implement APIs for innovative software systems.
• Excellent communication skills and the ability to collaborate effectively in a global, cross-functional team environment.

Preferred Qualifications

• Understanding of VLSI design, EDA flows, or digital implementation and signoff concepts.
• Experience with design space exploration, optimization methods, or performance-driven engineering workflows.
• Knowledge of basic probability theory and statistical modeling concepts.
• Exposure to applying machine learning techniques to chip design, system design, or other complex engineering domains.
• Able to evaluate deep learning research and apply relevant concepts to practical software development problems.