Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 2 years of experience programming in C and Kotlin.
- 1 year of experience with ML engineering (e.g., prompt engineering, model quality evaluations, model training, debugging, ML infrastructure).
- Experience with core GenAI concepts (e.g., LLM, Multi-Modal, Large Vision Models).
Preferred qualifications:- Master's degree or PhD in Computer Science or related technical fields.
- 2 years of experience with data structures and algorithms.
- Experience developing accessible technologies.
About the jobThe Contextual Search team's mission is to make Google Search more helpful by seamlessly answering questions based on users' context. We are re-wiring users' mental model about Search - instead of Search being a stateless, contextless utility, where all information needs to be entered into the search box, users will think of Search as a tool that is "aware" of the content they're consuming and provides answers to questions about that content.
We are leveraging foundational capabilities of LLMs to revolutionize Search - enable users to easily ask contextual questions and provide high-quality AI answers.
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: $147000 - $211000 (USD) 15% bonus target bonus equity benefits
Learn more about benefits at Google .
Responsibilities - Write product or system development code.
- Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency,)
- Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
- Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
- Implement GenAI solutions, utilize ML infrastructure, and contribute to data preparation, optimization, and performance enhancements.
