Senior Engineering Manager, ML Optimization Tools and Observability
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Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience with software development.
- 7 years of experience leading technical project strategy, ML design, and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 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.
- 5 years of experience with one or more of the following: ML performance, debugging, optimization, profiling, or observability.
- 5 years of experience in a people management or team leadership role.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 5 years of experience working in a complex, matrixed organization.
- Experience designing, developing and servicing enterprise products for third-party users with emphasis on reliability, scalability and ease of use.
About the job
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.
The two decades of investing into developer tools and infrastructure has dramatically boosted the productivity of Google Software Engineers. We are at the beginning of that journey for ML (model and stack) developers. The primary focus of our ML Performance and Observability Services team is to build the infrastructure and tools to help developers better understand their ML workloads and to quickly find performance, functional, numerical, and other types of issues with their models and infrastructure.
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 $248,000-$349,000 + 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.
Responsibilities
- Manage and build teams of software engineers who are developing debugging, observability, performance diagnostics, and other related tools and services to enable first-party and third-party ML developers to perform their tasks with the highest agility and efficiency.
- Develop the long-term technical goal and roadmap, ensuring both evolve to meet anticipated future requirements and infrastructure needs in the fast-moving ML space.
- Lead AI/ML technical strategy, large-scale infrastructure optimization, and specialized solution design for internal and third-party developers, leveraging Google's market-leading technology.
- Collaborate closely with cross-functional Product Managers, Technical Program Managers, peer Engineering Managers, as well as multiple Google product areas and GCP customers.
- Architect services with native JAX/PyTorch support for ML users, open-sourcing third-party services to cultivate a growing external developer community.
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Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.
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