Google
Google

ML Modeling Engineer

RoleMachine Learning
LevelMid Level
LocationBengaluru, India
WorkOn-site
TypeFull-time
Posted3 weeks ago
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About the role

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.

As a Silicon Engineer, you will work closely with architects, implementation designers, verification engineers and software engineers for providing bit-accurate system c-models and specifications. You will have experience in ML/AI accelerator development and a good understanding in SoC development along with software design expertise.

In this role, you will help develop c-model architecture for ISP ML accelerators. You will create and maintain a software environment for reference ML accelerator c-models. You will develop a system model that expands the team to work across organizations for ML accelerator integration.

Responsibilities

  • Create and drive software architecture for ML accelerator.
  • Implement, modeling, analyzing and testing ML accelerator.
  • Develop software environment for ISP ML accelerator development and simulation.
  • Lead on delivery of ISP ML accelerator c-models for implementation, verification.
  • Collaborate with partner teams to integrate the ML accelerator into compiler software environment.

Minimum qualifications:

  • Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience.
  • 8 years of experience in C or C++ or Python programming.
  • 8 years of experience in reference model development and silicon design trade-off.

Preferred qualifications:

  • Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
  • Experience with processor core architectures (such as ARM, x86, RISC-V, etc.) and IPs commonly used in SoC designs.

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