Software Engineer, Retrieval Engine

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.
Retrieval Engine's mission is to provide scalable, easy-to-use, cutting-edge information retrieval over all Google's data. It enables efficient, reliable, real-time retrieval over large-scale datasets containing multiple types of data, including embeddings, text, geo-locations, and numerics.
Retrieval Engine is used to power the worlds largest search engines such as , and others and we're building infrastructure and quality systems that feed GenAI model's context windows with relevant information (example applications are Search AI Mode, Search AI Overviews, and Gemini).
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits
Learn more about
benefits at Google
.

Minimum qualifications
Bachelor's degree or equivalent practical experience.
2 year of experience in coding with C++.
2 years of experience with distributed systems.

Preferred qualifications
Master's degree or PhD in Computer Science, or a related technical field.
2 years of experience with machine learning infrastructure.

Responsibilities
Improve the infrastructure that powers information retrieval and query understanding for surfaces like Gemini, and others.
Work on systems that are highly reliable, massively distributed and very low latency.
Scale and optimize systems to scale to even larger work loads and work even more efficiently.