Search users increasingly expect rich, visual, and personalized content in their AI Mode and AI Overview responses. Our team builds memory and personalization for multimodal queries, including Google Lens traffic and increasingly complex questions that may combine text, images, and other file inputs. Our tech stack combines classic retrieval/ranking of image results with LLM-native visual capabilities.
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: $262000 - $364000 (USD) + 25% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Set technical strategy and prioritization for the multimodal rich features domain.
- Solve deep technical issues collaboratively at the intersection of core quality, new features, infrastructural optimizations, and model training.
- Drive team execution and consistently deliver on top-line metrics.
- Spot opportunities proactively to connect the dots across multiple workstreams.
- Accelerate team members' career growth by establishing next-level scope and providing regular coaching.
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience in a technical leadership role, and 5 years managing people.
- 5 years of experience leading design for machine learning products and optimizing systems that incorporate machine learning.
- Experience integrating generative AI tools or large language model (LLM) interfaces into workflows.
Preferred qualifications:
- Experience supporting and developing the careers of engineering team members.
- Experience working on user-facing machine learning products.
- Experience setting technical direction and scaling technical impact on software engineering teams.
- Experience in data analysis with a metrics-first perspective and quality evaluation.