Software Engineering Manager, Drive Gen AI Quality and Context

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.

Google Drive is committed to creating a secure, intelligent, and universally accessible means of storing, sharing, organizing, and finding information. The immediate top focus is leveraging Generative AI to fundamentally revolutionize how users interact with their content, unlocking unprecedented value from their personal and enterprise knowledge.

in this role, you will leverage state-of-the-art AI to transform the vast knowledge stored in drive into features that supercharge user productivity. While your primary mandate is delivering user-facing features, you will also architect and maintain the critical backend infrastructure responsible for processing and serving derived data to power these AI experiences at scale.

AI will change the future of work in profound ways, and our products— Gmail, Docs, Drive, Calendar, Sheets, Vids and Meet are at the forefront. From pre-computed summaries for email threads, summaries for meetings, and videos created from a document using lifelike AI avatars, our AI opportunity is huge. Our mission is to meaningfully connect people so they can create, build, and grow together and as part of the team you can build how productivity tools should work 5-10 years into the future. You will work with model builders (Google DeepMind), work with exceptional leaders, and have the ability to impact billions of users across the world.

Responsibilities

• Lead a team of software engineers to deliver excellent, scalable, efficient, and sustainable products.
• Provide technical leadership and hold high technical excellence. Contribute thought leadership and system design opinions.
• Collaborate with other Engineering teams, Product Managers, and cross-functional leaders, and manage important deadlines.
• Exhibit strong product instincts, understand how the team's technical work meets the business needs for the customers. Negotiate with cross-functional partners to arrive at the best balance between engineering effort, system complexity, and customer value.
• Recruit top talent, develop and support your team. Provide an environment where everyone can do their very best work.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript).
• 3 years of experience in a technical leadership role.
• 2 years of experience in a people management or team leadership role.
• Experience with GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).

Preferred qualifications:

• 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.
• 5 years of experience leading ML design and optimizing end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).