Engineer, Design Verification Methodology, AI/ML, Flows
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Minimum qualifications:
- Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.
- 8 years of experience in Design Verification (DV).
- Experience in developing and deploying state of the art verification methodologies.
- Experience in any one of the hardware description languages (HDL) (e.g. Verilog/SystemVerilog).
- Experience with Python for scripting, automation, and data analysis in a verification context.
Preferred qualifications:
- Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
- Experience with AI/ML frameworks and tools such as TensorFlow, PyTorch, or ONNX, particularly in how they relate to hardware implementation and verification.
- Understanding of modern verification methodologies (e.g., UVM, Specman, Formal Verification).
- Understanding of AI/ML fundamentals, including various neural network architectures (CNNs, RNNs, Transformers), ML algorithms, and concepts like training, inference, quantization, and activation functions.
- Excellent programming skills in Python.
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
- Integrate Machine Learning (ML) techniques into existing verification flows to improve efficiency, coverage, and debug capabilities. This could involve using ML for intelligent test case generation, bug prediction, or coverage analysis.
- Research, evaluate, and deploy new commercial and open-source verification tools, with a particular focus on those that support or enhance the verification of AI/ML designs.
- Work closely with AI/ML algorithm developers and architects to understand model behavior, data dependencies, and potential verification challenges, ensure effective integration into the verification environment.
- Identify opportunities to automate aspects of AI/ML verification, reduce manual effort and accelerate verification cycles.
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