Google

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 industry ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 5 years of experience in a technical leadership role; overseeing projects, with 5 years of experience in a people management, supervision/team leadership role.
• Experience designing, developing, and servicing enterprise products for third-party users with emphasis on reliability, scalability, and ease of use.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a technical related field.
• 5 years of experience working in a complex, matrixed organization.
• Experience with one or more of the following: ML infrastructure, ML performance/optimization, ML architecture, or specialization in another related ML field.

About the Job

Like Google's own ambitions, the work of a Software Engineer goes way 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.

Two decades of investing into developer tools & infrastructure has dramatically boosted the productivity of Google engineers. We are at the beginning of that journey for ML (model and stack) developers. The primary focus of our ML Performance & 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 existing issues with their models and infrastructure.

The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.

We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.

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.

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

• Manage and build team(s) 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 vision 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 thriving external developer community.