Minimum qualifications:
- Bachelor's degree in Electrical Engineering, Computer Science, Image Processing, or equivalent practical experience.
- 5 years of experience in software development.
- 3 years of experience with AI/ML algorithms and tools, LLMs, deep learning, or natural language processing.
- Experience in programming with C++, Python and algorithms.
- Master's or Ph.D. with an emphasis on the Machine Learning domain.
- Experience writing Machine Learning (ML) algorithms (e.g. an understanding of diffusion model, NLP, image and goals).
- Experience in modern ML frameworks, e.g. JAX, Tensorflow or Pytorch.
- Experience in deploying ML models on a device.
- Experience optimizing compilers.
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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.
In this role, you will be responsible for tuning and evaluating the image quality of camera Image Signal Processor (ISP), display, and video codec subsystems. You will have an understanding of image processing, camera ISP, display, codec technologies, or image quality tuning and evaluation.
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
- Optimize ML model for edge computing to ensure compatibility and performance, including post-training quantization (PTQ) and quantization-aware training (QAT).
- Develop tool chains to drive the Generative Artificial Intelligence (GenAI) model optimization.
- Collaborate with the hardware accelerator teams for example TPU or GPU to verify the inference quality.
- Assist the AI/ML algorithm team in integrating models into computing and ensure alignment with hardware capabilities.