Job Description & How to Apply Below
We’re looking for an experienced Machine Learning Platform Engineer who will bring focus and subject‑matter expertise around designing and implementing machine learning infrastructure and automation tools (MLOps and Dev Ops). This is a unique opportunity to grow in the world of machine learning infrastructure and work with a team of passionate individuals committed to the mission of bringing ML to enterprise.
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 ongoing development in areas such as reinforcement learning, unsupervised learning and computer vision. You can find out more about our research areas at
Your responsibilities
Design, build, and optimize machine learning deployment tools and automation systems that operate the business’s data and ML applications.
Design and implement best practices and standards for data and machine learning pipelines across the organization.
Collaborate with engineers and machine learning researchers to automate code analysis, build, integration and deployment of ML applications.
Support applications and projects with infrastructure design decisions and monitoring solutions.
Build highly scalable, resilient cloud and on‑premise systems for hosting machine learning systems using state‑of‑the‑art technologies.
Ideal candidate
5+ years of experience designing and implementing distributed systems and machine learning systems.
Experience building and maintaining Dev Ops pipelines such as Jenkins or Git Hub Actions.
Experience with MLOps orchestration tools such as Airflow, Kubeflow, Dagster, Flyte, or Meta Flow.
In-depth knowledge of various stages of the machine learning application deployment process.
Experience building tools and applications to automate various infrastructure and Dev Ops tasks.
Proficiency with programming languages such as Python, Bash, or JavaScript.
Solid understanding of the UNIX operating system.
Experience implementing monitoring solutions to identify system bottlenecks and production issues.
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.
Hands‑on experience building and deploying hybrid environments on‑prem and major cloud environments, such as AWS and Azure.
Familiarity with machine learning frameworks such as PyTorch, Tensor Flow and/or similar.
What we offer
Be part of a team that thinks progressively and works collaboratively. We care about seeing each other reach full potential.
A comprehensive Total Rewards Program including bonuses, flexible benefits, competitive compensation, commissions, and stock options where applicable.
Leadership that supports your development through coaching and management opportunities.
Opportunity to make a difference and lasting impact from a local‑to‑global scale.
About RBC Borealis
RBC Borealis is the driving force behind Royal Bank of Canada’s AI and data innovation. As part of Canada’s largest financial institution, we bring together a team of architects, engineers, scientists, and product experts on a mission to revolutionize finance through world‑class research, solutions, and a resilient data platform. With locations across Toronto, Waterloo, Montreal, Calgary, and Vancouver, we’re at the forefront of AI research and platform development, focusing on cutting‑edge research in time‑series forecasting, causal machine learning, and responsible AI.
We seamlessly integrate AI research and data engineering to solve critical challenges in the financial industry and build intelligent, scalable, data‑driven solutions that help communities thrive and drive innovation for our customers across the bank.
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 veteran…
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