What's the opportunity?
We’re looking for an experienced DevOps Engineer who will bring focus and subject-matter expertise around designing, implementing, and supporting machine learning infrastructure and automation tools in production. 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:
- Providing production support and maintenance for ML applications;
- Designing, building, and optimizing machine learning deployment tools and automation systems that operate the business’s data and ML applications;
- 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 decisions, and monitoring solutions;
- Building highly scalable, resilient cloud and on-premise systems for hosting machine learning systems using state-of-the-art technologies;;
- Providing after-hours production support.
You're our ideal candidate if you have:
- Strong experience with Site Reliability Engineering, DevOps and production support;
- Strong and relevant experience supporting and maintaining distributed systems;
- Hands-on experience building and deploying virtualized environments in major cloud environments, such as Kubernetes, AWS and Azure;
- Experience with building tools and applications to automate various infrastructure tasks (Jenkins, etc.);
- Experience implementing monitoring solutions to identify system bottlenecks and production issues;
- Solid understanding of the UNIX operating system.
- Knowledge of the machine learning application deployment process;
- 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;
- 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.
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, 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.