Silicon Validation Engineer

Job description

Imagine what you can do here. Apple is a place where extraordinary people gather to do their lives best work. Together we create products and experiences people once couldn't have imagined, and now, can't imagine living without. It's the diversity of those people and their ideas that inspires the innovation that runs through everything we do. Minimum Qualifications: Master’s degree or foreign equivalent in Electrical Engineering, Electronics Engineering, or a related field. Experience and/or education must include: Designing and analyzing electronic circuits, including analog, mixed-signal, and power circuits, to support validation and characterization of silicon IPs and subsystems. Designing embedded systems using microcontrollers or FPGAs, including experience with hardware bring-up, peripheral communication interfaces (SPI, I2C, UART), and low-level hardware control for lab instrumentation and test platforms. Utilizing programming and scripting languages, including Python, C and Tcl, to develop, optimize, and maintain test automation scripts, measurement utilities, and lab tooling for analog and mixed-signal validation workflows. Performing signal analysis and processing, including time-domain and frequency-domain analysis of analog and mixed-signal waveforms, using tools such as NumPy, SciPy, and MATLAB to extract electrical parameters and characterize circuit behavior. Designing and maintaining data management systems and pipelines, including collecting, storing, querying, and visualizing large volumes of measurement data using SQL databases and data analytics tools to support silicon bring-up and tape-out decisions. Applying artificial intelligence and deep learning techniques, including using frameworks such as PyTorch or TensorFlow, to develop predictive models and automate pattern recognition in analog and mixed-signal measurement data. Utilizing PyTorch, TensorFlow, and OpenCV to design and train deep learning models, including convolutional neural networks (CNNs), for automating pattern recognition on analog and mixed-signal measurement data and developing computer vision pipelines for visual monitoring of lab equipment and automated chip handling. Preferred Qualifications: N/A