Tech Lead Manager, ML Accelerator Fleet Efficiency

Google•Sunnyvale, CA

1d•$207,000 - $300,000

About The Position

Like Google's own ambitions, the work of a Software Engineer goes beyond just Search. Software Engineering Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of Engineers. You not only optimize your own code but make sure Engineers are able to optimize theirs. As a Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team. Teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started -- and as a manager, you guide the way. With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally. Our team drives machine learning computational efficiency. We manage software to optimize the utilization of hundreds of thousands of Google Accelerator Units globally. We build the software abstraction layer between ML models and physical TPU/GPU hardware. Our mission is to eliminate resource waste across Google’s accelerator fleet, maximizing the physical utility of compute clusters while maintaining peak developer velocity and seamless runtime execution. We strive to provide a cohesive, highly efficient, and transparent runtime environment that enables ML teams to focus entirely on modeling and research rather than physical infrastructure constraints. 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. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits Learn more about benefits at Google [https://www.google.com/about/careers/applications/benefits/].

Requirements

Bachelor’s degree or equivalent practical experience.
8 years of experience in software development.
5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
3 years of experience in a technical leadership role.
2 years of experience in a people management or team leadership role.

Nice To Haves

Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
Experience with ML development, modeling, optimization, and infrastructure.
Experience with TPUs, TPU system design, and GPUs.
Experience with low-level programming.
Expertise in ML compilers and runtimes.

Responsibilities

Drive technical strategy, roadmaps, and adoption for large-scale ML infrastructure development.
Innovate next directions for infrastructure over a 12-month time horizon given a rapidly changing technology landscape.
Exercise sound engineering judgment to guide sustainable engineering choices for ML systems at scale.
Seek additional opportunities to drive efficiencies in ML workloads using scaling, idle suspend, and improving these capabilities with existing and novel technologies.
Lead a team of ~10 engineers to develop solutions that drive the efficiency of ML workloads.