Senior Software Engineer, Search Quality

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software engineering and Machine Learning.
• 8 years of experience in data science or other quantitative fields such as: statistics, machine learning, software engineering, or ML engineering.
• 5 years of experience working with Machine Learning, AI, and Large Language Models (LLM) to process and create data.

Preferred qualifications:

• Master's or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, or related technical field.
• Experience in AI algorithms.
• Experience in Machine Learning research.
• Experience in Machine Learning infrastructure.
• Experience with advanced algorithms.
• Excellent programming/software skills (e.g. C/C++ or Python).

About the job

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.

The US base salary range for this full-time position is $189,000-$284,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .

Responsibilities

• Develop a wide range of solutions, spanning from modeling to infrastructure (e.g., storing new knowledge, efficiently using LLMs to evaluate thousands of requests, improving those models, and enhancing their intelligence).
• Work with multiple codebases and machine learning libraries, creating numerous throwaway models and prototypes, as well as reusable model architectures and advanced libraries.
• Work in the context of real applications for important Google products and in partnership with product teams and other research engineers.