Job Description
What's the opportunity?RBC Borealis is at the forefront of artificial intelligence research and development, driving the innovative edge for the Royal Bank of Canada (RBC). Our team is dedicated to advancing the science of AI, delivering cutting-edge solutions that create substantial value for RBC and its customers. As part of RBC, you will have the opportunity to contribute to impactful projects within a leading financial institution, harnessing the power of AI to revolutionize the financial services industry.We’re looking for an enthusiastic software developer who’s excited by the opportunity of working on challenging problems at the intersection of machine learning and the financial services industry. As a Machine Learning Software Engineer, you’ll be responsible for owning and delivering a project end to end – everything from data pre-processing and exploration, to building and scaling ML algorithms and pipelines, to deployment and monitoring of production systems. At RBC Borealis, you’ll be joining a team that works directly with leading researchers in machine learning, has access to rich and massive datasets, and offers the computational resources to support cutting-edge machine learning R&D.Your responsibilities include:To build cutting edge ML solutions throughout the research and product development lifecycle;
To plan the work and lead a team of software engineers to deliver the machine learning products and solutions;
To play a key role in the design and development of Borealis’ machine learning products;
To partner with RBC Borealis’s research and product teams to ensure the seamless delivery of these products;
To apply engineering and data best practices to build robust and scalable large-scale machine learning software systems;
To support projects with thorough documentation, design decisions, and technical advisory.
Experience building modular, robust, scalable software for machine learning and/or data software systems in Python or similar language;
Experience leading a team of engineers through the full lifecycle of a project delivery;
Experience working across the entire research and product lifecycle from prototyping to production;
Exposure to distributed computing frameworks as well as containerization (eg. Docker, Kubernetes);
Knowledge of professional software engineering best practices for the full software development life cycle, including testing methods, coding standards, code reviews and source control management;
Experience working with data science tooling and deep learning frameworks is a plus.
Be part of a dynamic & flexible working environment;
Become part of a team that thinks progressively and works collaboratively. We care about seeing each other reach full potential;
Leaders who support your development through coaching and managing opportunities;
Ability to make a difference and lasting impact from a local-to-global scale.
Machine Learning Software Engineer II • Toronto, Ontario
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