Technical Lead Manager, Search Notification Quality

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in Machine Learning focussed on recommender systems.
• Experience with Data Mining.
• Experience with Personalization.
• Experience with Information retrieval.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  
• 3 years of experience in a technical leadership role leading project teams and setting technical direction.
  
• Experience with sequence based user understanding or sequential recommendation systems.
• Experience in development of modern ranking system such as implementation of various predicted click-through-rate (pCTR) based ranking framework.
• Experience in LLM empowered personalization or agentic based flow to help with improving recommendation performance for the systems.
• Experience with building Deep Learning models with the core TensorFlow/PyTorch/JAX.

About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

Billions of search notifications provide an entry point for 100+M users to search and Discover every day.

Search notification quality team owns modeling towards notification personalization and is one of the largest contributors to Search growth. Quality improvement projects from the team contributed to 10+% of growth for Search product area in 2025.

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.
The US base salary range for this full-time position is $197,000-$291,000 + 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.
The US base salary range for this full-time position is $197,000-$291,000 + 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

• Develop new opportunities and drive high-impact focused on innovation of adapting recommendation algorithms towards new applications and new surfaces.
• Evaluate the quantitative and qualitative effect of changes including end-to-end modeling for the whole personalization flow using retrieval, ranking, user understanding, and budgeting prediction for search notifications.
• Experiment with novel signals, labels, architectures, policies and training regimens. This involves both extensive model design and development work and a certain amount of plumbing work to make sure the data is where it needs to be.
• Work on modeling problems including sequence-based user understanding, real-time training, and personalization with L