Machine Learning Engineer

The ideal candidate for this role should hold a Bachelor's degree in Computer Engineering, Electrical Engineering, Computer Science, or a related field or possess equivalent practical experience. Additionally, you should have at least 5 years of experience working with ML/AI frameworks and libraries such as TensorFlow, PyTorch, and scikit-learn. It is essential to have a background in hardware description languages like Verilog, SystemVerilog, and VHDL, along with experience in applying ML/AI techniques. Preferred qualifications include hands-on experience with ML/AI applications in hardware design, verification, and Low Power, such as formal verification with ML and coverage closure with ML. Familiarity with verification methodologies like UVM and OVM is highly advantageous. Proficiency in data preprocessing, feature engineering, hardware architecture, microarchitecture, and simulation tools like Synopsys VCS, Cadence Xcelium, and Mentor Questa is preferred. Excellent programming skills in Python or C++ are also desirable. As a member of our team, you will be part of a group that continually pushes boundaries by developing custom silicon solutions that drive the future of Google's direct-to-consumer products. Your contributions will play a crucial role in innovating products that are adored by millions worldwide, shaping the next generation of hardware experiences to deliver unparalleled performance, efficiency, and integration. Google's mission is to organize the world's information and make it universally accessible and useful. Our team leverages the best of Google AI, Software, and Hardware to create incredibly helpful experiences. We are dedicated to researching, designing, and developing new technologies and hardware to enhance computing speed, seamlessness, and power, ultimately striving to enhance people's lives through technology. Your responsibilities will include researching, designing, and implementing ML/AI algorithms and techniques for various verification tasks, such as test case generation, coverage analysis, bug prediction, and performance optimization. You will be tasked with developing and maintaining tools and scripts for data collection, preprocessing, model training, and evaluation. Additionally, you will analyze large datasets of simulation results, logs, and other verification data to identify patterns and trends, building and training ML models for various verification applications, and evaluating model performance to enhance accuracy and efficiency. Furthermore, you will participate in verification planning, developing test plans that integrate ML/AI-driven techniques, executing verification tests, and analyzing results to pinpoint bugs and coverage gaps. You will also be responsible for developing and maintaining verification tools and scripts to automate verification tasks effectively.,