Machine Learning Engineer, Design Verification, Silicon

Google organizes the world's information and makes it universally accessible and useful, combining AI, Software, and Hardware.
Machine Learning
Mid-Level Software Engineer
In-Person
5,000+ Employees
5+ years of experience
AI

Description For Machine Learning Engineer, Design Verification, Silicon

Google is seeking a Machine Learning Engineer specializing in Design Verification for their Silicon team. This role sits at the intersection of ML/AI and hardware design, focusing on developing cutting-edge verification solutions for Google's custom silicon products. The position requires expertise in both machine learning frameworks and hardware verification methodologies.

The role involves creating and implementing ML/AI algorithms for various verification tasks, including test case generation, coverage analysis, and bug prediction. You'll work with large datasets of simulation results and verification data, building and training models for anomaly detection, pattern recognition, and performance optimization.

As part of Google's hardware team, you'll contribute to the development of custom silicon solutions that power Google's direct-to-consumer products. Your work will directly impact products used by millions of people worldwide, helping to shape the next generation of hardware experiences with a focus on performance, efficiency, and integration.

The ideal candidate should have a strong foundation in computer science or engineering, with significant experience in ML/AI frameworks like TensorFlow and PyTorch. Knowledge of hardware description languages and verification methodologies is crucial. You'll need to combine technical expertise with problem-solving skills to develop innovative solutions for complex verification challenges.

This position offers the opportunity to work at the forefront of hardware innovation, combining the best of Google's AI, software, and hardware capabilities. You'll be part of a team that pushes boundaries and creates radically helpful experiences, making computing faster, seamless, and more powerful while contributing to Google's mission of organizing the world's information and making it universally accessible.

Last updated 18 hours ago

Responsibilities For Machine Learning Engineer, Design Verification, Silicon

  • 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, preprocessing, 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

Requirements For Machine Learning Engineer, Design Verification, Silicon

Python
  • 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
  • Experience with ML/AI applications in hardware design, verification and Low Power (preferred)
  • Experience with verification methodologies (UVM, OVM) (preferred)
  • Experience in data preprocessing and feature engineering, hardware architecture and microarchitecture (preferred)
  • Experience with simulation tools (preferred)
  • Excellent programming skills in Python or C++ (preferred)

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