Principal Engineer, Foundations Performance - Google
Sunnyvale, CA
About the Job
Minimum qualifications:
- Bachelor’s degree in Computer Science, or similar technical field of study or equivalent
- 15 years of professional experience as a software engineer or 13 years with an advanced degree.
- Experience with technical innovation in Frameworks, Libraries, Tools, APIs, or related fields.
Preferred qualifications:
- 20 years of professional experience.
- Technical expertise in systems and software with the leadership skills needed to influence technical leaders across the company.
- Expertise in reasoning and quantifying relative impacts and risks of technical work that could require the involvement of thousands of engineers.
- Understanding of the needs of advanced ML developers.
- Ability to quickly ramp up in new subject areas with short notice to guide a few weeks of mission-critical research into the largest potential risks related to Google infrastructure usage.
- Excellent systems design skills.
About the job
The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.
Core Machine Learning (ML) is focused on driving ML excellence for Google. This organization brings teams together and will help simplify and make it easier for ML experimentation, development, and productionization, and align infrastructure initiatives with new areas of research and product innovation. This helps us better meet the challenge of the rapidly evolving hardware and software space around ML.
As the Principal Engineer, you will lead the design of large-scale and versatile ML systems. You will be a technical lead who works across business units at Google to assess the needs and constraints for very large-scale ML and then drive the system design.
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
The US base salary range for this full-time position is $278,000-$399,000 + 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
- Build and enable the large machine learning (ML) models. Build techniques and methodologies for seamless software scalability and reliability.
- Work cross-functionally with Google product teams to assess needs and constraints to design the appropriate systems.
- Develop a structure of other technical leads in the area, both by defining technical goals and orienting teams around technical decisions they can make, and by providing development support to engineers in the area.
- Develop distributed systems for model training (e.g., SPMD, model parallelism, data parallelism) and serving. Develop flexible ML infrastructure for various processor types (e.g., CPU/GPU/TPU/XLA).
- Act as a executive reviewer or point of contact for large-scale changes or technical decision-making, participate in calibration and promotion feedback, and drive appropriate decision making and technical review processes for their areas and their broader teams.