Director of Engineering, Machine Learning Fleet and Capacity Full-time

Minimum qualifications:

• Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience.
• 15 years of experience as an engineering leader.
• Experience building software tools related to enterprise capacity planning.

Preferred qualifications:

• Experience in the iterative development model.
• Experience motivating others at all levels by creating a shared sense of vision and purpose.
• Experience with ideation and innovation of exceptional technology at scale, and passion for development and the use of cross-platform shared code.
• Expertise in collaborating with partners and customers to solve problems.
• Expertise in engaging with senior stakeholders and managing competing opinions and viewpoints.
• Ability to balance detailed, technical guidance with big picture strategy, enabling teams to deliver products that are effective and also creating new ways to manage data.

About the job

Google’s Machine Learning, Systems and Cloud AI (MSCA) organization builds the technical foundation behind Google’s products as well as for Google Cloud’s compute and AI/ML offerings. We manage the underlying design elements, developer platforms, product components, and infrastructure at Google. Core Machine Learning (Core ML) is the central machine learning platform team that provides Machine Learning software tools, services, solutions, and infrastructure to all the Google product areas, including Search, Ads, YouTube, Google Cloud, Maps, etc. Core ML is focused on Driving ML Excellence for Google and the world. Our aim is to make it easier to perform ML experimentation, development, and productionization, and we work closely with Google Research and DeepMind to bring new ML models and innovation across the stack to market. This enables us to better meet the challenge of the rapidly evolving hardware and software space around ML.

In this role, you will provide leadership to the ML Fleet (planning) and Resource Economy (fulfillment and re-allocating) teams in MSCA. You’ll partner closely with all of Google’s Product Areas, including Research and Core Pillars, developing and building engineering partnerships based on deep engagements and towards fructifying end-to-end journeys. You’ll provide organizational leadership to navigate organizational complexities and technical leadership to foster trusting and productive conversations with other engineering leaders.

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

• Lead a growing engineering organization responsible for architecting and building technical solutions to efficiently deploy, manage, and monitor Machine Learning capacity and resources at Google.
• Collaborate and support in some of the most critical strategic ML programs at Google. These include enabling our next generation foundational ML model training and deployment, enabling our customers to rapidly adopt our next generation ML hardware platforms, and maximizing the impact of our ML infrastructure to the most important Google Product Areas and priorities.
• Develop and grow talent through effective mentoring, development, coaching, and inspiring with a strong focus on talent management and instilling a data-driven culture that succeeds in a fast-paced environment.
• Own and set the strategy, roadmap, and implementation for multiple areas of focus, balancing short- and long-term priorities against various business needs.