Google Research
Locations

Montreal

We perform research in the areas of deep learning, reinforcement learning and optimization, applied to various problems including program synthesis, game playing, few-shot learning, generative modeling and machine translation.

Google Research in Montreal performs both open-ended and applied research, in numerous areas including reinforcement learning, meta-learning, optimization, program synthesis, generative modeling, machine translation, and more. We also support the local academic community and have several academic collaborations, including with Mila – Quebec Artificial Intelligence Institute.

Research areas

Machine Intelligence

Machine Translation

Some of our teams

Google Brain Team

Language

Some of our Work

Reducing the Computational Cost of Deep Reinforcement Learning Research
Reducing the Computational Cost of Deep Reinforcement Learning Research

Revisiting the Rainbow algorithm to demonstrate that despite emphasis on large-scale environments, small-scale environments can still yield valuable scientific insights and can help reduce the barriers to entry for underprivileged communities.

JAXopt
JAXopt

Hardware accelerated (GPU/TPU), batchable and differentiable optimizers in JAX.

Drifting Efficiently Through the Stratosphere Using Deep Reinforcement Learning
Drifting Efficiently Through the Stratosphere Using Deep Reinforcement Learning

How Loon and Google AI achieved the world’s first deployment of reinforcement learning in a production aerospace system.

Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning
Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning

A large-scale and diverse benchmark for measuring the competence of different image classification models in a realistic and challenging few-shot setting.

Improving Sparse Training with RigL
Improving Sparse Training with RigL

An algorithm for training sparse neural networks that identifies neurons that should be active during training, which helps the optimization process to utilize the most relevant connections and results in better sparse solutions.

Using ML-based systems in Songwriting
Using ML-based systems in Songwriting

Findings on what musician/developer teams needed when creating a song using machine learning, the challenges they faced, and how they leveraged and repurposed existing characteristics of such systems to overcome some of these challenges.

Implicit Gradient Transport Optimizer
Implicit Gradient Transport Optimizer

A TensorFlow implementation of the Implicit Gradient Transport optimizer, which reduces the variance in online optimization by transporting past gradients.

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

A new benchmark for training and evaluating few-shot learning models that is large-scale, consists of diverse datasets, and presents more realistic tasks.

Framework for Flexible and Reproducible Reinforcement Learning Research
Framework for Flexible and Reproducible Reinforcement Learning Research

Introducing a New Framework for Flexible and Reproducible Reinforcement Learning Research

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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Software Engineer
Software 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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Internship
Internship

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