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Machine Learning System Engineer Jobs in Toronto, ON

As a System Engineer, you provide support for a suite of business mission-critical and complex ... Experience on Machine Learning projects. * Experience deploying, monitoring, and debugging ...

What you'll do As a machine learning engineer, you will be responsible for analyzing opportunities ... How do we evaluate a system offline & online? * How do we improve performance to match (and beat ...

We develop and deploy industry-leading machine learning systems that impact the lives of over 27 ... Collaborate closely with our engineering team in a fast-paced startup environment and see your ...

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Machine Learning System Engineer information

What is a machine learning system engineer?

Machine learning system engineers are professionals who design, build, and maintain the infrastructure and systems that support machine learning models in production environments. They work at the intersection of software engineering and data science, ensuring that machine learning algorithms run efficiently, scale appropriately, and integrate seamlessly with existing applications. Their responsibilities often include data pipeline development, model deployment, monitoring, and optimization to ensure reliable and robust AI solutions.

What are the key skills and qualifications needed to thrive as a machine learning system engineer?

To thrive as a Machine Learning System Engineer, you need strong skills in computer science, statistics, machine learning algorithms, and a degree in a related field such as computer science or engineering. Proficiency with programming languages like Python or Java, experience with ML frameworks (e.g., TensorFlow, PyTorch), and knowledge of cloud platforms are typically required. Exceptional problem-solving abilities, teamwork, and effective communication are vital soft skills that help in designing scalable solutions and collaborating across teams. These skills ensure the successful development, deployment, and maintenance of reliable machine learning systems in real-world environments.

What are some common challenges machine learning system engineers face when deploying models to production environments?

Machine Learning System Engineers often encounter challenges such as ensuring model scalability, maintaining low latency, and addressing data drift once models are deployed in production. They must also work closely with software engineers, data scientists, and DevOps teams to integrate models seamlessly into existing systems and monitor their ongoing performance. Additionally, balancing computational resources and optimizing for cost efficiency while ensuring high reliability can be complex, making collaboration and clear communication essential in this role.

What is the difference between Machine Learning System Engineer vs Data Scientist?

AspectMachine Learning System EngineerData Scientist
CredentialsBachelor's or Master's in CS, ML, or related fields; certifications in ML or cloud platformsBachelor's or Master's in Statistics, Data Science, or related fields; certifications in data analysis or ML
Work EnvironmentDevelops, deploys, and maintains ML systems; collaborates with engineering teamsAnalyzes data, builds models, interprets results; works closely with business teams
Industry UsageTech companies, AI startups, enterprises deploying ML systemsResearch institutions, analytics firms, tech companies

While both roles involve machine learning, Machine Learning System Engineers focus on building and maintaining scalable ML systems, whereas Data Scientists primarily analyze data and develop models for insights. The roles often overlap but differ in technical focus and responsibilities.

Infographic showing various Machine Learning System Engineer job openings in Toronto, ON as of September 2026, with employment types broken down into 1% As Needed, 83% Full Time, 13% Part Time, and 3% Contract. Highlights an 91% Physical, 2% Hybrid, and 7% Remote job distribution.

Machine Learning Software Engineer

Toronto, ON

Royal Bank of Canada
Banking and Credit Intermediation • 10K+ employees

Full-time

Re-posted 2 days ago


Job description

Job Description


What's the opportunity?

RBC Borealis is 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 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.

You're our ideal candidate if you have:

  • Experience building modular and robust software systems in Python or similar language;

  • 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 across the entire ML research and product lifecycle from prototyping to production is a plus;

  • Experience building microservices, data pipelines and using relational and non-relational databases is a plus;

  • Experience working with data science tooling and deep learning frameworks is a plus;

  • Experience with DevOps engineering (CI/CD pipelines, observability, containers etc) is a plus.

What's in it for you?

  • 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.

About the AI Group

RBC's AI Group is the AI accelerator for RBC, with a focus on driving the shift from early-stage AI projects to scaled, client outcomes that amplify the impact of RBC's people. In addition to helping scale the biggest AI opportunities at RBC, the AI Group is responsible for advancing research into emerging use cases across generative and agentic AI, while maintaining expertise in security, responsible AI and regulatory expectations. The Business Enablement function within the AI Group partners with LOBs and Functions to set AI ambition, originate transformation opportunities, and frame programs for delivery - ensuring RBC remains at the frontier of AI-enabled value creation.

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.

RBC is presently inviting candidates to apply for this existing vacancy. Applying to this posting allows you to express your interest in this current career opportunity at RBC. Qualified applicants may be contacted to review their resume in more detail.

#Ll-POST

#TechPJ

Job Skills

Big Data Analytics, Critical Thinking, Decision Making, Industry Knowledge, Machine Learning (ML), Software Engineering, Software Product Design

Additional Job Details

Address:

777 BAY ST, TH 27:TORONTO

City:

Toronto

Country:

Canada

Work hours/week:

37.5

Employment Type:

Full time

Platform:

HUMAN RESOURCES & BMCC

Job Type:

Regular

Pay Type:

Salaried

Posted Date:

2026-07-27

Application Deadline:

2026-09-30

Note: Applications will be accepted until 11:59 PM on the day prior to the application deadline date above

Our Employment Opportunities

At RBC, we are guided by living shared values of Client First, Integrity, Collaboration, Respect and Excellence and winning together as One RBC. We believe an inclusive workplace that has diverse perspectives is core to our continued growth as one of the largest and most successful banks in the world. Maintaining a workplace where our employees feel supported to perform at their best, effectively collaborate, drive innovation, and grow professionally helps to bring our Purpose to life and create value for our clients and communities. RBC strives to deliver this through policies and programs intended to foster a workplace based on respect, belonging and opportunity for all.

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Employment Type: FULL_TIME