ML Software Engineer Intern - AI/CAD Integration

Are you passionate about applying machine learning to transform the future of semiconductor design? At Falcomm, we are on a mission to revolutionize semiconductor technologies by integrating AI-driven solutions into the design and development of our energy-efficient power amplifier products. As a ML Software Engineer Intern in AI/CAD Integration, you will collaborate with our team of experts to develop and deploy AI/ML models that enhance CAD workflows, automate design processes, and accelerate innovation in RFIC design. This role offers a unique opportunity to work at the intersection of software engineering, machine learning, and semiconductor design, contributing directly to tools and methodologies that drive next-generation hardware solutions.

We are seeking an intern who thrives in a fast-paced environment, is eager to tackle complex technical challenges, and is motivated to turn AI research into practical engineering solutions. If you have a strong foundation in machine learning, software development, and a desire to apply these skills to advance semiconductor innovation, we invite you to join us. At Falcomm, your creativity and technical expertise will help shape energy-efficient technologies that set new industry standards.

Responsibilities

• Design and implement code for training/serving ML models in chip design workflows
• Automate creation of large datasets for training models
• Integrate ML models into existing chip design software infrastructure
• Write comprehensive tests and documentation
• Collaborate with engineers to understand model requirements and deployment needs
• Participate in code reviews and follow software engineering best practices
• Research and evaluate new technologies for ML model serving and deployment

Requirements

• Currently pursuing a degree in Computer Science, Electrical Engineering, or related field
• Availability to work at least part-time starting in Fall 2026
• Strong Python programming skills with experience in scripting and automation
• Understanding of software engineering principles and code organization
• Experience with data processing libraries (pandas, numpy, scipy)
• Familiarity with machine learning frameworks (PyTorch, TensorFlow, scikit-learn)
• Basic understanding of file I/O, data parsing, and format conversion
• Experience with version control systems (Git) and collaborative development

Preferred Skills

• Exposure to CAD tools or EDA software (Cadence, Synopsys, Mentor Graphics, etc.)
• Knowledge of chip design file formats (GDSII, LEF/DEF, Verilog, SPICE)
• Experience with shell scripting (bash) and command-line tools
• Understanding of semiconductor design concepts