Engineering Manager, Workspace Search Quality
Confirmed on the employer's own hiring board on Sep 26, 2026. First seen by Alion on Aug 18, 2026.
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.
We build intuitive, consistent search tools across Google Workspace (e.g., Gmail, Drive, Chat, Calendar) to help over a billion users find content quickly, reliably, and confidently. We are driving a major strategic shift from keyword matching to advanced Artificial Intelligence (AI) search leveraging semantic retrieval, natural language understanding, multi-modal search, and contextual recommendations to shape the future of productivity and collaboration.
In this role, you will have the opportunity to shape the future of workspace search for over a billion active users.
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.
Responsibilities
- Advocate a culture of data-driven improvement. Oversee system debugging, performance tuning, and technical debt management to ensure robust and reliable production environments.
- Partner with the Technical Lead to define and drive the technical roadmap, while maintaining strong technical leadership and oversight for search and recommendation systems. Ensure the end-to-end reliability and quality of models from data ingestion and indexing to serving.
- Drive the development of advanced 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. Use offline/online evaluation, user feedback signals to continuously improve search relevance and user satisfaction.
- Foster a high-performing and engineering culture. Provide active coaching, performance feedback, and personalized career development to empower engineers at all levels.
Qualifications
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience with managing and growing a team of software engineers, with experience in performance management and career development.
- 5 years of experience in a people management, supervision/team leadership role.
- Experience with software engineering in Information Retrieval (IR), Natural Language Processing (NLP) or Machine Learning (ML).
- Experience with designing, implementing, and optimizing large-scale, high-performance, distributed search or quality systems in production environments.
Preferred qualifications:
- Master's degree or PhD in technology or equivalent practical experience in Computer Science, Machine Learning, Natural Language Processing, Information Retrieval, or a related technical field.
- Experience with embedding-based retrieval (EBR), vector search, query understanding (qNLU), or large language models (LLMs) applied to search/recommendation systems.
- Experience managing system latency improvements and scalability enhancements.
- Good understanding of agentic architectures, tool-calling, and Retrieval-Augmented Generation (RAG) grounding layers.
- Exceptional collaboration and communication skills, with a proven ability to align cross-functional stakeholders and navigate competing priorities.