Research Engineer, AI Studio Compute Efficiency

Google; United States; On-site; USD 207,000 - 300,000; Full time

Job summary

Google DeepMind is seeking a Research Engineer for Gemini API and Beyond, focusing on compute efficiency and scalable AI solutions. You will work with product managers, engineers, and researchers to design, develop, and deploy high-performance AI systems across multiple platforms.

You will help quantify usage, identify efficiency opportunities, and contribute to analytics infrastructure while aligning with Google's AI strategy and safety standards.

Qualifications

• Bachelor's degree in CS, Mathematics, Statistics, Machine Learning, Behavioral Science, or equivalent practical experience.
• Experience programming in Python or C++.
• Experience working in industry, taking projects from proof-of-concept through to implementation.
• Experience applying experimental ideas to applied problems using statistical methods.
• Experience working on a product team.

Responsibilities

• Lead reporting of compute costs and utilization for platforms like AI Studio, Gemini API, and Beyond.
• Research and develop quantitative approaches to improve fleet-wide compute efficiency and tie patterns to strategy.
• Contribute to reporting and analytical infrastructure, including dashboards and workflows.
• Present complex research findings to stakeholders in a compelling way.
• Collaborate across DeepMind to support cross-product journeys for developers.

Skills

Python

C++

Industry experience

Statistics & experimental ideas

Product team collaboration

Education

Bachelor's degree in CS/Math/Stats/ML or equivalent

Master's degree or PhD in a related field

Tools

Front-end design

Job description

Google DeepMind is seeking a Research Engineer for Gemini API and Beyond, focusing on compute efficiency and scalable AI solutions. You will work with product managers, engineers, and researchers to design, develop, and deploy high-performance AI systems across multiple platforms.

You will help quantify usage, identify efficiency opportunities, and contribute to analytics infrastructure while aligning with Google's AI strategy and safety standards.