Lead and innovate in the realm of Workspace Search Quality, driving a significant strategic shift towards advanced AI-powered search. You will be instrumental in shaping the future of how over a billion users find information, moving beyond keyword matching to leverage semantic retrieval, natural language understanding, and multi-modal search.

This role offers a unique opportunity to influence the trajectory of productivity and collaboration tools. You will work at the forefront of AI's impact on the future of work, building the next generation of intuitive and consistent search experiences for Google Workspace applications like Gmail, Drive, and Calendar. Join a team that is redefining productivity for the next 5-10 years.

Foster a culture centered on data-driven enhancements. You will be responsible for overseeing system debugging, performance optimization, and technical debt management to ensure production environments are both robust and reliable.

Collaborate closely with the Technical Lead to define and execute the technical roadmap, providing strong leadership for search and recommendation systems. Ensure the end-to-end quality and reliability of models, from data ingestion through to serving.

Drive the advancement of sophisticated Information Retrieval and Machine Learning systems, including embedding-based search, ranking algorithms, and natural language query understanding. Define and implement rigorous evaluation frameworks and success metrics, utilizing offline/online evaluations and user feedback to continuously elevate search relevance and user satisfaction.

Cultivate a high-performing engineering environment. Provide active coaching, constructive performance feedback, and personalized career development to empower engineers at all stages of their careers.

A Bachelor's degree or equivalent practical experience is required, alongside 8 years of experience in managing and developing software engineering teams, including expertise in performance management and career development. A minimum of 5 years in a people management or team leadership capacity is essential.

Demonstrated experience in software engineering within Information Retrieval (IR), Natural Language Processing (NLP), or Machine Learning (ML) is crucial. You should also possess experience in designing, implementing, and optimizing large-scale, high-performance, distributed search or quality systems within production settings.

Preferred qualifications include a Master's degree or PhD in a relevant technical field such as Computer Science, Machine Learning, Natural Language Processing, or Information Retrieval. Experience with embedding-based retrieval, vector search, advanced query understanding, or large language models applied to search systems is highly beneficial. Experience in managing system latency improvements and scalability enhancements is also valued, as is a strong understanding of agentic architectures, tool-calling, and Retrieval-Augmented Generation grounding layers. Exceptional collaboration and communication skills are vital for aligning cross-functional stakeholders and navigating complex priorities.