EDA and Machine Learning Intern

We Are:Drive technology innovations that shape the way we live and connect. Our technology drives the Era of Pervasive Intelligence, where smart tech and AI are seamlessly woven into daily life. From self-driving cars and health-monitoring smartwatches to renewable energy systems that efficiently distribute clean power, Synopsys creates high-performance silicon chips that help build a healthier, safer, and more sustainable world.

At Synopsys, interns dive into real-world projects, gaining hands-on experience while collaborating with our passionate teams worldwide-and having fun in the process! You'll have the freedom to share your ideas, unleash your creativity, and explore your interests. This is your opportunity to bring your solutions to life and work with cutting-edge technology that shapes not only the future of innovation but also your own career path. Join us and start shaping your future today!

Our mission is to fuel today's innovations and spark tomorrow's creativity. Together, we embrace a growth mindset, empower one another, and collaborate to achieve our shared goals. Every day, we live by our values of Integrity, Excellence, Leadership, and Passion, fostering an inclusive culture where everyone can thrive-both at work and beyond.

• Develop strategies for evaluating the similarity between standard cell data in the same library or different libraries, using approaches like custom distance metrics or embeddings derived from existing DNN models.
• Improve existing metadata encodings (such as cell function or process corner) to enhance DNN model performance and create a content-based information retrieval prototype.
• Use the content-based information retrieval prototype to identify suitable data for model training and develop an approach for using this data within a transfer learning framework to quickly update the existing DNN model.
• Develop approaches for compressing the final DNN model after transfer learning.

• Creative, entrepreneurial, and independent problem solver, proficient at navigating the uncertainty associated with transforming research into products.
• Experience working with state-of-the-art deep learning models, including image segmentation, generative techniques such as GANs and diffusion, and active or reinforcement learning.
• Knowledge of Python and associated ML libraries (Pytorch, Tensorflow, Pandas, Numpy, Sklearn).
• Experience with large-scale scientific computing, including distributed computing and databases.
• Familiarity with IC digital design, analog design, SOC architecture, and IC validation is a plus.
• Strong interpersonal skills for interfacing with both internal teams and customers.
• In the process of completing a PhD in Electrical Engineering or Computer Science.

Program Length:12 weeks

Location:Austin, TX

Working Model:Hybrid, In-office

Full-Time/Part-Time:Full-time

Start Date:May / June 2025

Synopsys is committed to creating an inclusive workplace and is an equal opportunity employer. We welcome all qualified applicants to apply, regardless of age, color, family or medical leave, gender identity or expression, marital status, disability, race and ethnicity, religion, sexual orientation, or any other characteristic protected by local laws. If you need assistance or a reasonable accommodation during the application process, please reach out to us.