AI/CAD Software Engineer

We are seeking a full-time AI/CAD Software Engineer to join our team. You will lead the development of intelligent tools that enhance RF workflows, automate complex EM simulations, and accelerate the tape-out of high-performance PAs. This role sits at the intersection of software engineering, machine learning, and RF physics, perfect for a developer who wants to get close to the hardware.

- End-to-End ML Pipeline Development: Architect and maintain robust code for training and serving ML models that predict and optimize RF performance metrics. - PA Design Automation: Automate the creation and management of large-scale datasets derived from PA load-pull and electromagnetic (EM) simulations. - Tool Integration: Seamlessly integrate ML inference engines into existing RF EDA infrastructure to provide real-time feedback to PA designers. - Software Engineering Excellence: Establish best practices including comprehensive testing, CI/CD pipelines, and code reviews within an agile hardware team. - Cross-Functional Collaboration: Partner closely with RF/PA architects to translate complex impedance matching and linearity requirements into deployable software solutions.

- Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field. - 2+ years of professional experience in software engineering or machine learning. - Proficiency in Python: Advanced scripting, object-oriented programming, and automation skills. - ML Framework Expertise: Experience building models using PyTorch, TensorFlow, or scikit-learn. - Data Engineering: Strong grasp of data processing libraries (pandas, numpy, scipy) and file I/O operations. - Software Fundamentals: Solid understanding of algorithms, data structures, and version control (Git). - RF Domain Knowledge: RF front-end architectures, or a strong understanding of RF metrics (PAE, Gain, ACLR, EVM).

- RF PA Domain Knowledge: Experience with Power Amplifier design and an understanding of RF PA metrics. - RF/EM Simulation Tools: Hands-on experience with Cadence Virtuoso, Ansys HFSS, or Keysight ADS. - Open Source Innovation: Experience utilizing or contributing to open-source RF/EDA alternatives (e.g., gdsfactory, KLayout, Qucs, openEMS). - Simulation Scripting: Familiarity with automating RF simulations via SKILL, AEL, or Python APIs. - Physics-Informed ML: Interest or experience in applying ML to physics-based problems or electromagnetic modeling.