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Salary
≈ $147k – $286k per year (Estimated)
Location
In office (Mountain View)
Seniority
Middle · 4+ years exp

Confirmed on the employer's own hiring board on Sep 26, 2026. First seen by Alion on Aug 24, 2026.

Overview
Company
Impact
Profile match
Google is an American technology company founded in 1998 by Larry Page and Sergey Brin and now the principal subsidiary of Alphabet, headquartered in Mountain View, California. It operates the world's dominant search engine and the advertising system built around it, along with YouTube, Android, Chrome, Gmail, Maps, Workspace and Google Cloud, reaching billions of users across nearly every internet-connected market. The company designs its own silicon in the Tensor Processing Unit line, develops the Gemini foundation models through Google DeepMind, and derives most of its revenue from advertising while cloud has become its fastest growing segment.

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.

In this role, you will create and maintain a robust software environment for reference ML accelerator c-models. You will build system models that enable cross-functional collaboration and drive ML accelerator integration across organizations. You will work closely with hardware architects, implementation designers, verification engineers, and software teams to deliver bit-accurate system c-models and specifications. You will have proven experience in ML/AI accelerator development, a strong understanding of SoC architecture, and solid software design expertise.

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.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $138000 - $197000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

  • Architect and drive the software infrastructure and architecture for on-device AI/ML accelerators.
  • Implement, model, analyze, and rigorously test C-models for on-device ML accelerators.
  • Develop and maintain robust software environments to support ML accelerator development, testing, and simulation.
  • Lead the end-to-end delivery of sophisticated ML accelerator C-models, directly supporting hardware implementation and verification efforts.
  • Collaborate cross-functionally with hardware architects, design verification, firmware, and compiler teams to build cycle-approximate models for ML accelerators.

Qualifications

Minimum qualifications:

  • Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience.
  • 4 years of experience with software development using C, C++ or Python.
  • Experience in AI/ML accelerator architecture or reference C-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 silicon engineering and performance characterization.
  • Experience in reference C-model development, performance analysis, and the use of simulation frameworks and profiling tools.
  • Experience with CPU or ML accelerator architectures.
  • Programming proficiency in C/C++ and Python, specifically applied to system-level modeling and performance optimization.
  • Exceptional problem-solving abilities and excellent communication skills, with a proven track record of dissecting complex issues and fostering cross-functional collaboration with engineering teams and leadership.
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