Senior Tech Lead Manager, Search Discover Sequence Modeling and Generative Retrieval

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.

Discover is Google's personalized interest feed. The Discover User Modeling and Retrieval team builds foundational user interest models by learning from trillions of daily activities. These models capture long-term interests and in-the-moment intent to power Discover and drive personalization across multiple Google products.

As a Senior Tech Lead Manager for the Discover Sequence Modeling team, you will lead the development of next-generation recommendation models. You will oversee Discover's foundational long-term user model, drive extra-long-sequence modeling, and pioneer Semantic-ID generative retrieval. You will partner with DeepMind and Infrastructure teams to build frontier models, launch them at Google scale, and shape the future of personalization for users.

Join Google Search AI and work at the intersection of industry leading

global scale. We apply breakthrough research, leveraging models like Gemini, to launch high-impact products that help billions ask any question and get things done. If you want to drive the future of AI in Search and work with world-class experts to deliver tangible, global impact and continuous innovation, this is the place.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $262000 - $364000 (USD) + 25% bonus target + equity + benefits

Learn more about benefits at Google . Responsibilities

• Lead and grow an executive team of engineers across multiple workgroups, fostering a high-velocity engineering culture.

• Set and own the multi-year technical strategy for next-generation retrieval and foundational user modeling, including long-sequence models and Semantic-ID generative retrieval.

• Translate research into production launches, managing quality, serving-cost, and freshness trade-offs to drive engagement metrics.

• Co-develop generative recommendation models, Large Language Model (LLM) post-training, and continuous training infrastructure in partnership with external research teams.

• Report technical strategy, live-experiment results, and project risks to leadership while aligning cross-functional partner teams.

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 end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).

• 5 years of experience in a people management or team leadership role.

• Experience building large-scale recommendation, retrieval, search, or ads machine learning systems.

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 with long-sequence user modeling, Semantic IDs, generative retrieval, or Large Language Model (LLM) fine-tuning applied to recommendation systems.

• Experience utilizing JAX/TPUs, continuous training, or distributed training with large embedding tables.

• Experience taking research-grade models into production at scale or publishing at top Machine Learning and Information Retrieval venues.