Machine Learning Researcher
What’s the opportunity?
Borealis AI is a team of researchers and developers dedicated to solving today’s leading problems in machine learning and artificial intelligence. Our researchers are dedicated to pushing the boundaries of theoretical and applied science, while our development team transforms state-of-the-art technologies and algorithms into impactful products with the potential to reach millions of people.
As a Machine Learning Researcher, you’re looking to channel your love of playing with real- world data into industry-disrupting solutions. We’re a lab that supports open academic collaborations with leading universities in Canada and the US on a wide variety of theoretical and applied machine learning projects. Working in our lab 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 also have the opportunity to publish original research in peer-reviewed academic journals and participate in conferences around the world, such as NIPS, ICLR, ICML, CVPR and more.
Your responsibilities include:
- Performing literature reviews in areas of focus and presenting state-of-the-art findings to the team.
- Conducting original research in Deep Learning, Reinforcement Learning, Unsupervised Learning, Time-Series Analysis, Natural Language Processing, Optimization, Visualization and more.
- Formulating and testing hypotheses quickly and efficiently.
- Drafting, editing and publishing academic papers in leading scientific conferences.
- Collaborating with RBC’s data scientists over the use of novel ML algorithms for data analytics
- Working with the development team to transfer research results into production.
- Teaching team members about latest developments in Machine Learning.
You’re our ideal candidate if you have:
- A passion for data, algorithms, and statistics.
- A PhD or MSc. in computer science, engineering or another mathematically related field (e.g. Physics, Math, Statistics, etc.).
- Experience with writing modular, robust, scalable software in Python 3.x.
- Familiarity with the Unix command line and bash scripting.
- Proficiency with Deep Learning packages such as Tensorflow, Theano, Keras and PyTorch.
- Exposure to distributed computing frameworks (e.g. Hadoop, Spark) as well as SQL, NoSQL and graph databases.
- The ability to frame problems, research solutions, and successfully apply generalizable models.
- A deep understanding of machine learning algorithms and/or statistical modeling.
- Experience in two or more of the following areas: Traditional Machine Learning, Deep Learning, Natural Language Processing, Time-Series Analysis, Computer Vision and/or Reinforcement Learning.
- Publications in fields relevant to data science or machine learning.
How to apply:
Please email your CV and GitHub (or equivalent) portfolio to email@example.com and include where you heard about this opportunity.
What’s in it for you?
- Become part of a team that thinks progressively and works collaboratively. We care about seeing each other reach full potential.
- Academic freedom and massive datasets.
- A comprehensive Total Rewards Program including bonuses and flexible benefits, competitive compensation, commissions, and stock options where applicable.
- Leaders who support your development through coaching and managing opportunities.
- Ability to make a difference and lasting impact from a local-to-global scale.
About Borealis AI
Borealis AI, a RBC Institute for Research, is a curiosity-driven research centre dedicated to achieving state-of-the-art in machine learning. Established in 2016, and with labs in Toronto, Edmonton, Montreal, Waterloo and Vancouver, we support open academic collaborations and partner with world-class research centres in artificial intelligence. With a focus on ethical AI that will help communities thrive, our machine learning scientists perform fundamental and applied research in areas such as reinforcement learning, natural language processing, deep learning, and unsupervised learning to solve ground-breaking problems in diverse fields.