Principal Engineer, Core ML Infra, Training and Experimentation
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Minimum qualifications:
- Bachelor’s degree in Computer Science, similar technical field of study, or equivalent practical experience
- 15 years of professional experience as a software engineer
- Experience with ML technologies and infrastructure with regard to training, bulk inference, and experimentation
- Experience with technical innovation in one or more relevant domains (e.g. AI/ML)
Preferred qualifications:
- Master's degree in Computer Science or similar technical field of study
- 20 years of professional software engineering experience, or 15 years with an advanced degree
- Acumen in machine learning coupled with exceptionally strong API and systems design skills and strong understanding of the needs of advanced ML developers
- Ability to quickly ramp up new subject areas with short notice
- Deep technical expertise in systems and software, including leadership skills
- Expertise in reasoning and quantifying relative impacts and risks of technical work that could involve engineers
About the job
Core ML is a sub team in the Google AI and Infrastructure organization which is responsible for delivering compute, platforms, and tools powering Google's AI-first products, research, and cloud at scale, price, and efficiency. Core ML's mission is to drive ML excellence for Google and the world. The organization strives to make it easier to create a well lit and efficient path for AI experimentation, development and productionization, aligning 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 AI. As the Core ML Training & Experimentation Principal Engineer, you will lead design and development of the Core ML training, bulk inference, and experimentation infrastructure. You will drive the technical strategy and road map across the training ecosystem for Core ML. You will engage closely with researchers, applied ML leads, and product UTLs to design and develop the best ML bulk inference and experimentation ecosystem, extending Google's leadership position in AI. You'll also drive ML velocity efforts across Google.
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 $294,000-$414,000 + 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
- Provide leadership in designing and implementing user-friendly, high-performance experimentation, bulk inference stack, and ecosystem for research and Google products.
- Work cross-functionally with Google Research and the Product team to design developer solutions to streamline the research to production for large models. Drive ML velocity to enable quick iteration cycles for research and product teams.
- Develop a structure of other technical leads in the area, by defining technical goals and orienting teams around decisions.
- Provide leadership in firefighting situations or situations that require complex and immediate triaging, technical problems, and other emergency work. Help managers of associated teams understand the larger technical issues.
- Act as a senior reviewer or go-to person for large-scale changes or technical decision-making. Participate in calibration and promotion feedback, and develop/drive appropriate decision-making and technical review processes.
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