Borealis AI is an AI and Machine Learning Research Institute backed by Royal Bank of Canada. Comprised of top AI researchers and engineers and motivated by the pursuit of solving intelligence, Borealis AI is advancing machine learning science, while building leading products for financial services. With 40+ scientific publications in top-tier academic venues, the institute performs research in areas, such as deep learning, reinforcement learning, language processing, AI safety, and more. Borealis AI was founded in 2016 and has approximately 100 team-members across its four labs in Canada.
Inclusion and Equal Opportunity Employment
RBC is an equal opportunity employer committed to diversity and inclusion. We are pleased to consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veterans status, Aboriginal/Native American status or any other legally-protected factors. Disability-related accommodations during the application process are available upon request.
Borealis AI offers research internships across labs. Interns support research on a wide variety of theoretical and applied machine learning projects. Working in our labs will grant you unique access to massive structured and unstructured datasets with the tools and resources necessary to build game-changing statistical models. Being part of our team means you’ll have the opportunity to publish original research in peer-reviewed academic conferences, such as NeurIPS, ICLR, ICML, CVPR. And you’ll be working with some of the brightest minds in AI.
Internship opportunities are available in the following areas:
Graphs and Optimization
Unsupervised and Semi-supervised Learning
Privacy and Fairness
Interpretability and Explainability
Time Series Forecasting
Natural Language Processing
You’re our ideal candidate if you have:
Ability to implement state-of-the-art machine learning techniques
High motivation to solve challenging research problems
Passion for data, algorithms, and statistics
Experience working towards a PhD or already hold a MSc in Computer Science, Engineering or another mathematically related field (e.g., Physics, Math, Statistics, etc.)
Previous publications at a top-tier AI conference
Experience with writing modular, robust, scalable software in Python
Familiarity with the Unix command line and bash scripting
Proficiency with deep learning packages, such as Tensorflow, Keras, and PyTorch
A deep understanding of machine learning algorithms and/or statistical modeling