Google Research
Locations

Paris

We tackle big challenges across several fields at the intersection of computer science, statistics and applied mathematics while collaborating closely with a strong academic community.

Our Paris team covers a variety of areas within computer science and mathematics with a strong focus on machine learning —from algorithmic foundations and theoretical underpinnings of deep learning to operations research, mathematical programming, reinforcement learning, natural language processing, machine perception, data compression and computational biology.

Research areas

Algorithms and Theory

Human-Computer Interaction and Visualization

Machine Intelligence

Machine Perception

Some of our teams

AI Fundamentals and Applications

Algorithms & Optimization

Brain Team

Operations Research

Responsible AI

Some of our work

A Simulation Suite for Tackling Applied Reinforcement Learning Challenge
A Simulation Suite for Tackling Applied Reinforcement Learning Challenge

The RWRL suite is a set of simulated tasks inspired by applied reinforcement learning challenges, the goal of which is to enable fast algorithmic iterations for both researchers and practitioners, without having to run slow, expensive experiments on real-systems.

Exploring Faster Screening with Fewer Tests via Bayesian Group Testing
Exploring Faster Screening with Fewer Tests via Bayesian Group Testing

We present an approach to group testing that can operate in a noisy setting to decide adaptively by looking at past results which groups to test next, with the goal to converge on a reliable detection as quickly, and with as few tests, as possible.

Learning Cross-Modal Temporal Representations from Unlabeled Videos
Learning Cross-Modal Temporal Representations from Unlabeled Videos

We propose to learn temporal representations from unlabeled videos. The goal is to discover high-level semantic features that correspond to actions and events that unfold over longer time scales.

Sudoku, Linear Optimization, and the Ten Cent Diet
Sudoku, Linear Optimization, and the Ten Cent Diet

Google's linear optimization library is freely available for the public and can be used to create a delicious meal.

The cpu_features library
The cpu_features library

cpu_features is a small, fast, and simple open source library to report CPU features at runtime. Written in C99 for maximum portability, it allocates no memory and is suitable for implementing fundamental functions and running in sandboxed environments.

Investing in France's AI Ecosystem
Investing in France's AI Ecosystem

We're launching a new AI research team in our Paris office as we continue to expand Google's research presence in Europe.

Making France's digital potential work for everyone
Making France's digital potential work for everyone

Our research team works closely with the AI research community in France on issues like health, science, art and the environment.

Work with us

Research Scientist
Research Scientist

Whether developing experiments, prototyping implementations, or designing new architectures, Research Scientists work on real-world problems in computer science. Our Research Scientists work across data mining, natural language processing, hardware and software performance analysis, improving compilation techniques for mobile platforms, core search, and much more.

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Research Engineer
Research Engineer

Our research-focused software engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. From creating experiments and prototyping implementations to designing new architectures, research engineers work on machine learning, data mining, hardware and software performance analysis, improving compilers for mobile platforms and much more.

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Research Intern
Research Intern

Our technical interns are key to innovation at Google and make significant contributions through applied projects and research publications. Internships take place throughout the year, and we encourage students from a range of disciplines, including CS, Electrical Engineering, Mathematics, and Physics to apply to work with us.

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We believe open collaboration is essential for progress

We’re proud to work with academic and research institutions to push the boundaries of AI and computer science. Learn more about our student and faculty programs, as well as our global outreach initiatives.

See outreach programs