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).
- Master's degree or PhD in Computer Science or 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.
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About the job
Our computational challenges are so big, complex and unique we can't just purchase off-the-shelf hardware, we've got to make it ourselves. Your team designs and builds the hardware, software and networking technologies that power all of Google's services. As a Hardware Engineer, you design and build the systems that are the heart of the world's largest and most powerful computing infrastructure. You develop from the lowest levels of circuit design to large system design and see those systems all the way through to high volume manufacturing. Your work has the potential to shape the machinery that goes into our cutting-edge data centers affecting millions of Google users.
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.