Senior Staff Software Engineer, Workspace Search Quality
Summary
Builds and improves AI-powered search for Google Workspace (Gmail, Drive, Docs, etc.), using machine learning and NLP to enhance relevance and user experience for billions of users.
We are building Intuitive, and effective searching and finding experiences by enabling search journeys that bridge across Google Workspace products (e.g., Gmail, Chat, Drive, etc.). We have an ambitious roadmap for making Workspace Search excellent, including the usage of advanced Machine Learning (ML) technologies, significant user-facing changes, revamping our evaluation framework and more.AI will change the future of work in profound ways, and our products— Gmail, Docs, Drive, Calendar, Sheets, Vids and Meet are at the forefront. From pre-computed summaries for email threads, summaries for meetings, and videos created from a document using lifelike AI avatars, our AI opportunity is huge. Our mission is to meaningfully connect people so they can create, build, and grow together and as part of the team you can build how productivity tools should work 5-10 years into the future. You will work with model builders (Google DeepMind), work with exceptional leaders, and have the ability to impact billions of users across the world.
- Lead the architecture, design and implementation of end-to-end search solutions, from understanding natural language queries to developing and deploying machine learning models that significantly improve search quality.
- Drive innovation by exploring and integrating machine learning techniques, algorithms, and architectures to enhance search relevance and user experience.
- Own the technical goal and roadmap for critical areas of Workspace Search, collaborating with cross-functional teams to define and deliver on a strategic roadmap.
- Establish and refine evaluation frameworks and metrics to measure and track progress against key performance-indicators.
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software engineering with Information Retrieval (IR), Natural Language Processing (NLP) or Machine Learning (ML).
- Experience with launching user-facing, large-scale, production quality systems.
- Experience designing, implementing, and optimizing large-scale, high-performance, distributed search or quality systems in production environments.
Preferred qualifications:
- Experience with embedding-based retrieval, vector search, query understanding or Large Language Models (LLMs) applied to search/recommendation systems.
- Experience mamaging system latency improvements (e.g., progressive loading, perfecting techniques, or bypassing post-retrieval latency overhead) and scalability enhancements.
- Background in defining metrics, analyzing user-perception surveys, and running complex search experiments.
- Good understanding of agentic architectures, tool-calling, Retrieval-Augmented Generation (RAG) grounding layers.
- Exceptional collaboration and communication skills, with a proven ability todrive technical alignment with cross-functional stakeholders and navigate competing priorities.