Software Engineering Tech Lead Manager, Search Intelligence, Discover User Understanding
Confirmed on the employer's own hiring board on Nov 4, 2026. First seen by Alion on Nov 4, 2026. Google scores B on the Alion truth index.
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.
Discover User Modeling and Retrieval builds Google's foundational knowledge of what people care about, and uses it to power Discover. These models drive hundreds of billions of recommendations a day in Discover, are critical personalization signals in downstream ranking, and feed personalization across Google, including Search Suggest, News, Search notifications and Shopping.
You will lead the team that owns Discover's interpretable, token-based user models, including Long-Term Interest and Creator models. These are the models that build the billions of interest profiles above, and the ones other Google products rely on.
In Google Search, we're reimagining what it means to search for information - any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally.
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.
Responsibilities
Manageand grow a team of engineers, mixing senior technical leads and early-career engineers, while developing each engineer's career and building a high-velocity team culture.
- Partner closely with DeepMind and Infra teams, and own our models end-to-end, from research ideas to production launches, at a scale few places can offer.
- Set and own the multi-quarter technical strategy and roadmap for token-based user modeling with the team's tech leads, product management, and partner teams.
- Drive the rebuild of interest-vocabulary redesign, unification of the token models, and the next-generation model architecture by making sound technical trade-offs and running thoroughdesign reviews.
- Raise the bar on experimentation and evaluation by investing in offline evaluation that predicts online results (including LLM-based autoraters), faster iteration loops, and disciplined live-experiment and launch practices.
Qualifications
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development, including 5 years in machine learning, recommendation, personalization, ads or search quality.
- 2 years of experience in a people management or team leadership role.
- Experience training, evaluating, or launching models for recommender systems.
- Experience with applied machine learning for recommendation, retrieval, or search platforms.
Preferred qualifications:
- Experience re-architecting production ML systems while maintaining downstream compatibility and stability.
- Management experience, leading a team of 5 or more engineers.
- Knowledge of modern recommender-system and ML techniques (e.g., user modeling, embedding-based and two-tower retrieval, sequence models and Transformers, distillation, multi-task and multi-objective learning, and applying LLMs to recommendation).
- A track record of leading ML projects from design to launch that require coordinating multiple teams.
- Excellent written and verbal communication, and the ability to align stakeholders with different priorities.