Image Signal Processing Machine Learning Modeling Engineer

3 Months ago • 5 Years + • Artificial Intelligence • $150,000 PA - $223,000 PA

Job Summary

Job Description

Google is looking for an Image Signal Processing Machine Learning Modeling Engineer to join their Devices and Services team. The engineer will be responsible for creating and delivering software architecture for Machine Learning (ML) accelerators, implementing, modeling, analyzing and testing ML accelerators, developing the software environment for Image Signal Processing (ISP) ML accelerator development and simulation, leading the delivery of ISP ML accelerator c-models for implementation, verification, and collaborating with partner teams to integrate the ML accelerator into compiler software environment. The ideal candidate will have a Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, 5 years of experience in silicon engineering, experience in C or C++ or Python programming, and experience in AI/ML accelerator or model development.
Must have:
  • Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field
  • 5 years of experience in silicon engineering
  • Experience in C or C++ or Python programming
  • Experience in AI/ML accelerator or model development
Good to have:
  • Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture
  • Experience in reference model development and silicon design trade-off

Job Details


Minimum qualifications:

  • Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience.
  • 5 years of experience in silicon engineering.
  • Experience in C or C++ or Python programming.
  • Experience in AI/ML accelerator or model development.

Preferred qualifications:

  • Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
  • Experience in reference model development and silicon design trade-off.

About the job

Our Devices and Services team combines the best of Google AI, Software, and Hardware to create helpful experiences for users. We research, design, and develop new technologies and hardware to make our user's interaction with computing faster, seamless, and more powerful. Whether finding new ways to capture and sense the world around us, advancing form factors, or improving interaction methods, the Devices and Services team is making people's lives better through technology.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.

The US base salary range for this full-time position is $150,000-$223,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about .

Responsibilities

  • Create and deliver software architecture for Machine Learning (ML) accelerator.
  • Implement, model, analyze and test ML accelerator.
  • Develop the software environment for Image Signal Processing (ISP) ML accelerator development and simulation.
  • Lead the delivery of ISP ML accelerator c-models for implementation, verification.
  • Collaborate with partner teams to integrate the ML accelerator into compiler software environment.

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