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Google
Google

Organizing the world's information and making it universally accessible.

Senior Software Engineer, ML Infrastructure, Cloud AI

RoleEngineering
LevelSenior
WorkOn-site
TypeFull-time
Posted1 month ago
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About the job

The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.

The US base salary range for this full-time position is $174,000-$252,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. 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

  • Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.

  • Understand how accelerator compiler and runtimes interact at a high level.

  • Close infra gaps to help with end-to-end Machine Learning (ML) stack maturation (e.g., reduce a number of ways something is done, improve reproducibility, improve tooling, improve usability).

  • Develop and apply metrics to understand the problem you are solving and gage status/success as needed.

  • Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.

Minimum qualifications

  • Bachelor’s degree or equivalent practical experience.

  • 5 years of experience with software development in C++.

  • 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.

  • 3 years of experience with machine learning infrastructure, distributed systems or networks, or experience with compute technologies, storage or hardware architecture.

  • Experience with high performance computing.

Preferred qualifications

  • Master's degree or PhD in Computer Science or a related technical field.

  • Experience in compilers or runtimes.

  • Experience with low-level programming.

  • Experience in graphics processing unit (GPU) programming.

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About Google

Google

Google

Public

Google specializes in internet-related services and products, including search, advertising, and software.

10,001+

Employees

Mountain View

Headquarters

$1,700B

Valuation

Reviews

10 reviews

4.5

10 reviews

Work-life balance

3.2

Compensation

4.3

Culture

4.1

Career

4.2

Management

3.8

82%

Recommend to a friend

Pros

Great benefits and perks

Innovative and interesting work

Career development and learning opportunities

Cons

High pressure and expectations

Long hours and heavy workload

Fast-paced and overwhelming environment

Salary Ranges

57,503 data points

Mid/L4

Mid/L4 · Accessibility Analyst

1 reports

$214,500

total per year

Base

$165,000

Stock

-

Bonus

-

$214,500

$214,500

Interview experience

9 interviews

Difficulty

3.4

/ 5

Duration

14-28 weeks

Offer rate

44%

Experience

Positive 0%

Neutral 56%

Negative 44%

Interview process

1

Application Review

2

Online Assessment/Technical Screen

3

Phone Screen

4

Onsite/Virtual Interviews

5

Team Matching

6

Offer

Common questions

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Product Sense