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Google

Machine Learning Software Engineer, Silicon

San Diego, CA

Minimum qualifications:

  • Bachelor's degree or equivalent practical experience.
  • 8 years of experience in software development, and with data structures/algorithms.
  • 5 years of experience with design and architecture, and testing/launching software products.
  • Experience with Machine Learning.
Preferred qualifications:
  • PhD in Computer Science.
  • Experience in running a large program or several projects simultaneously.
  • Experience in computer architecture for accelerators such as Machine Learning (ML) accelerators, Graphics Processor Unit (GPU), or Digital Signal Processor (DSP).
  • Understanding of how a parallelizing optimizing compiler works.

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About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

EdgeTPU is a family of embedded Machine Learning (ML) accelerators aiming towards a broad set of applications, from smartphones to self-driving cars to data center applications. We are developing a template design to aim the broad span of speed/energy dissipation/cost trade-offs corresponding to the many devices being developed. The Compute software team makes the Edge TPU ML accelerator programmable, via tooling that includes a compiler, runtime, SDK with documentation and further tooling, and an Applied ML team that optimizes ML models for serving on device.

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 $237,000-$337,000 + 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 benefits at Google .

Responsibilities

  • Work with thehardware architecture and software compiler teams. Help identify trade-offs for flexibility vs performance to help set direction to hardware design.
  • Enhance the current Tensor Processing Unit (TPU) programming model for advanced users. Design new mechanisms to support user-guided compilation to extract maximum performance out of the hardware.

Client-provided location(s): San Diego, CA, USA; Mountain View, CA, USA
Job ID: Google-119166318172611270
Employment Type: Full Time

Perks and Benefits

  • Health and Wellness

    • Health Insurance
    • Dental Insurance
    • Vision Insurance
    • Life Insurance
    • Short-Term Disability
    • Long-Term Disability
    • FSA
    • HSA
    • Fitness Subsidies
    • On-Site Gym
    • Mental Health Benefits
  • Parental Benefits

    • Birth Parent or Maternity Leave
    • Non-Birth Parent or Paternity Leave
    • Fertility Benefits
    • Adoption Assistance Program
    • Family Support Resources
    • Adoption Leave
  • Work Flexibility

    • Hybrid Work Opportunities
  • Office Life and Perks

    • Commuter Benefits Program
    • Casual Dress
    • Pet-friendly Office
    • Snacks
    • Some Meals Provided
    • On-Site Cafeteria
  • Vacation and Time Off

    • Paid Vacation
    • Paid Holidays
    • Personal/Sick Days
    • Leave of Absence
    • Volunteer Time Off
  • Financial and Retirement

    • 401(K) With Company Matching
    • Company Equity
    • Performance Bonus
    • Financial Counseling
  • Professional Development

    • Tuition Reimbursement
    • Internship Program
  • Diversity and Inclusion

    • Employee Resource Groups (ERG)

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