We’re looking for an experienced Infrastructure Engineer who will bring focus and subject-matter expertise around designing and implementing machine learning infrastructure and automation tools. 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 Borealis AI, 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 borealisai.com.
Your responsibilities include:
Designing, building, and optimizing machine learning deployment tools and automation systems that operate the business’s data and ML applications
Designing and implementing best practices and standards for data and machine learning pipelines across the organization
Collaborating with engineers, and machine learning researchers to automate code analysis, build, integration and deployment of ML applications.
Supporting applications and projects with infrastructure design decision, and monitoring solution
Building highly scalable, resilient cloud and on-premise systems for hosting machine learning systems using state-of-the-art technologies;
You're our ideal candidate if you have:
Strong and relevant experience designing and implementing distributed systems and Machine Learning systems;
In-depth knowledge of various stages of the machine learning application deployment process;
Experience with building tools and applications to automate various infrastructure tasks;
Solid understanding of the UNIX operating system;
Implementing monitoring solutions to identify system bottlenecks and production issues;
It would be great if you also have:
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 virtualized environments in major cloud environments, such as AWS and Azure;
Familiarity with machine learning frameworks such as PyTorch, TensorFlow, Keras, Theano, and/or similar.
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;
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
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, Montreal, Waterloo and Vancouver, we support 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.
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.