Research Engineer, Gemini API and Beyond Serving Compute Efficiency, DeepMind

• Lead the reporting of compute costs and utilization, while implementing critical business, product, and engineering metrics for platforms like AI Studio, Gemini API, and Beyond.
• Research and develop quantitative approaches to improve fleet-wide compute efficiency, and connect these usage patterns to product and business strategy.
• Contribute to innovative reporting and analytical infrastructure, including the use of agentic workflows and advanced dashboarding techniques.
• Present and communicate complex research findings in a compelling manner to a varied set of stakeholders.
• Collaborate across DeepMind (GDM) organizations to support high-quality, cross-product journeys for developers.

Minimum qualifications:

• Bachelor's degree in Computer Science, 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.

Preferred qualifications:

• Master's degree or PhD/DPhil in a related technical or scientific field.
• Experience applying and productionizing state-of-the-art large visual, language, and multimodal research, coupled with a passion for improving developer experience and generative AI.
• Proficiency or experience with front-end design skill.
• Understanding of compute allocation and serving architecture.
• Excellent data visualization and communication skills, with a track record of cross-functional collaboration and working effectively on a product team.

• Bachelor's degree in Computer Science, 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.