Research Engineer, AI Studio: Compute & Analytics

Google DeepMind; Mountain View (CA); On-site; USD 210,000 - 300,000; Full time

Benefits offered by this job

Bonus target

Equity

Benefits

Job summary

Google DeepMind is seeking a highly skilled professional to join its AI Studio and Gemini API team in Mountain View. You will conduct research and analysis that informs product strategy and collaborate with product managers, engineers, and researchers to design, develop, and deploy scalable, high-performance AI solutions.

You will help understand usage patterns, identify efficiency opportunities, and support decisions around compute allocation and production strategy, contributing to ambitious,

Qualifications

• Bachelor's degree 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, and implement metrics for platforms like AI Studio and Gemini API.
• Research and develop quantitative approaches to improve fleet-wide compute efficiency and relate usage to product strategy.
• Contribute to reporting and analytical infrastructure, including dashboards.
• Present complex research findings to stakeholders.
• Collaborate across DeepMind to support high-quality cross-product journeys for developers.

Skills

Python

C++

Industry experience

Experimental ideas

Product team experience

Education

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

Job description

Google DeepMind is seeking a highly skilled professional to join its AI Studio and Gemini API team in Mountain View. You will conduct research and analysis that informs product strategy and collaborate with product managers, engineers, and researchers to design, develop, and deploy scalable, high-performance AI solutions.

You will help understand usage patterns, identify efficiency opportunities, and support decisions around compute allocation and production strategy, contributing to ambitious,