Machine Learning Engineer, Design Verification, Silicon

Minimum qualifications: Bachelor's degree in Computer Engineering, Electrical Engineering, Computer Science, or related field, or equivalent practical experience. 5 years of experience with ML/AI frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn). Experience with hardware description languages (e.g., Verilog, SystemVerilog, VHDL). Experience with applying ML/AI techniques. Preferred qualifications: Experience with ML/AI applications in hardware design, verification and Low Power (e.g., formal verification with ML, coverage closure with ML). Experience with verification methodologies (UVM, OVM). Experience in data preprocessing and feature engineering, hardware architecture and microarchitecture. Experience with simulation tools (e.g., Synopsys VCS, Cadence Xcelium, Mentor Questa). Excellent programming skills in Python or C++. About the job Be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's direct-to-consumer products. You'll contribute to the innovation behind products loved by millions worldwide. Your expertise will shape the next generation of hardware experiences, delivering unparalleled performance, efficiency, and integration. Google's mission is to organize the world's information and make it universally accessible and useful. Our team combines the best of Google AI, Software, and Hardware to create radically helpful experiences. We research, design, and develop new technologies and hardware to make computing faster, seamless, and more powerful. We aim to make people's lives better through technology. Responsibilities Research, design, and implement ML/AI algorithms techniques for various verification tasks, including test case generation, coverage analysis, bug prediction, and performance optimization. Develop and maintain tools and scripts for data collection, pre-processing, model training, and evaluation. Analyze large datasets of simulation results, logs, and other verification data to identify patterns and trends. Build and train ML models for various verification applications, such as anomaly detection, pattern recognition, and prediction. Evaluate model performance and iterate to improve accuracy and efficiency. Participate in verification planning and develop test plans that incorporate ML/AI-driven techniques. Execute verification tests and analyze results to identify bugs and coverage gaps. Develop and maintain verification tools and scripts to automate verification tasks.