Silicon Software Engineer, Machine Learning, Edge TPU
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Minimum qualifications:
- Bachelor’s degree in Computer Science, related technical fields, or equivalent practical experience.
- 2 years of experience with software development in C++/Python, or 1 year of experience with an advanced degree.
- 2 years of experience with data structures or algorithms.
- Experience working with deep learning frameworks (e.g., Tensorflow, Pytorch, JAX).
Preferred qualifications:
- Master's degree or PhD in Computer Science or a related technical field with an emphasis on Machine Learning/Artificial Intelligence.
- Experience in developing and training machine learning models.
- Publications or research experience in deep learning.
- Ability to have a flexible mindset, practice default enthusiasm/optimism, and grow in a fast-paced environment.
About the job
We are the team that builds Google Tensor - Google’s custom System-on-Chip (SoC) that powers the latest Pixel phones. Tensor makes transformative user experiences possible with the help of cutting-edge Machine Learning (ML) running on Tensor TPU. Our goal is to enable varied ML solutions on-device by optimizing ML models utilizing Network Attached Storage (NAS), optimizing inference of large genAI models, developing edge-optimized foundational models, and implementing/developing SOTA techniques, tools, and workflows for machine learning model optimizations.
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
- Work on developing and training edge optimized models for genAI, computer vision, natural language, and speech use cases.
- Develop model optimization tools and infrastructure modules needed for automating customization and training of neural networks and architecture design space exploration.
- Work collaboratively with researchers and application developers to customize neural network architectures.
- Write modular and efficient ML training pipelines and assist in building profiling and visualization tools.
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