Our product management and business development team gets to solve real problems for RBC and its 17 million clients, delivering impact at scale. This team moves quickly, challenges the status quo, and delivers AI-based products that shape the future of banking.
How We Build
We scope, develop and deploy AI products grounded in research and ethical approach to AI. What drives us is the opportunity to build products that directly impact millions of RBC clients and create material value for RBC – at scale.
Our product, research, and engineering teams get to collaborate with expert cross-functional technical and business teams at RBC and tackle banking’s most challenging problems, influencing the bank’s AI strategy in the process.
-
Aiden
Reinforcement learning applied to electronic trading | Capital Markets
View Product
-
NOMI Forecast
AI for digital money management | Personal finance
View Product
-
Turing by Borealis AI
An interpretable text-to-SQL database interface for getting insights from relational databases without coding | Natural Language Processing
View Product
-
Prism
An AI-powered stock market simulation | Time Series
View Product
Advancing Responsible AI
Robustness
Adversarial robustness is key to developing deep learning models: our Advertorch Python toolbox contains adversarial training scripts, modules to generate adversarial perturbations, and more.
Engineering
Engineering plays a vital role at Borealis AI, turning our machine learning models into products that generate exponential business value. We understand the financial system we operate in, and we have the resources, knowledge and tools to improve it.
Our team of 40+ team engineers work with leading Machine Learning researchers to accelerate, build and deploy products that will enhance risk applications, reduce fraud, help millions of people manage cash flow, access credit and plan for their financial futures. In the finance sector, millions of people are counting on us to get it right.
-
Embracing complexity
Engineers at Borealis AI solve challenging engineering problems in complex financial services domains. They get to work with a large customer set, extensive data, and significant computation resources of Canada’s biggest bank, and build best-in-class products and solutions for millions of clients.
-
Tech stack
Typical tech stack includes: Python, PyTorch, NVIDIA GPU’s, cloud, Tech Radar, and more. Ask us about Borealis AI Common Platform our engineering team has recently launched!
-
Building products across business domains
Machine Learning in finance is evolving quickly. Engineers often get to go beyond specific projects to build products and POCs across a wide variety of business domains.
-
Cross-functional collaboration
The culture of cross-functional collaboration at Borealis AI means engineers work closely with world-class AI researchers, product managers, business development, and RBC partners.
Engineering Co-op
Borealis AI Engineering team runs a popular Co-op program where Machine Learning Software Engineers are involved in projects end to end, from data pre-processing to implementing machine learning algorithms and front-end development. Recruitment for this program is handled by RBC.