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Scientific Machine Learning Jobs in Ontario (NOW HIRING)

Senior Machine Learning Engineer

Toronto, ON ยท On-site

CA$170K - CA$250K/yr

As a Machine Learning Engineer, you will: * Join a world-class team of AI developers with an ... Master or bachelor's degree in computer science, Statistics, Mathematics, Engineering or a related ...

Applied Machine Learning Scientist I

Toronto, ON ยท On-site +1

CA$105K - CA$125K/yr

About the Role We are looking for a highly motivated Applied Machine Learning Scientist to join the Wealth AI / ML Practice, focused on Generative AI and Agentic Capabilities. In this role, you will ...

Senior Machine Learning Engineer

Toronto, ON ยท On-site

CA$105K - CA$125K/yr

... data scientist team members that assist them in building and optimizing our product into an ... The role of a machine learning professional involves applying machine learning techniques and ...

Showing results 41-60

Scientific Machine Learning information

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are the key skills and qualifications needed to thrive as a scientific machine learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What are some common challenges faced by professionals in scientific machine learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Scientific Machine Learning jobs in Ontario?

For Scientific Machine Learning jobs in Ontario, the most frequently searched job titles are:

What job categories do people searching Scientific Machine Learning jobs in Ontario look for?

The top searched job categories for Scientific Machine Learning jobs in Ontario are:

Infographic showing various Scientific Machine Learning job openings in Ontario as of September 2026, with employment types broken down into 14% Internship, and 86% Full Time. Highlights an 100% In-person job distribution.

Senior Machine Learning Software Engineer

Toronto, ON โ€ข On-site

Royal Bank of Canada
Banking and Credit Intermediationย โ€ขย 10K+ employees

Full-time

Re-posted 15 days ago


Job description

Job Description

What's the opportunity?

At RBC Borealis, you'll be joining a team of leading researchers and software engineering specializing in machine learning. You will have access to rich and massive datasets, and to computational resources to support novel product development touching machine learning areas such as generative AI, natural language processing, and time series analysis.

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 Senior 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:

  • Lead development of machine learning-based software 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:

  • A degree in Computer Science, Software Engineering, or equivalent field;
  • 7+ years of experience as a software engineer;
  • 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?

  • 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 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.


#LI-POST
#TECHPJ

Job Skills

Big Data Analytics, Client Counseling, Coaching Others, 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:

TECHNOLOGY AND OPERATIONS

Job Type:

Regular

Pay Type:

Salaried

Posted Date:

2026-07-03

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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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.

Employment Type: FULL_TIME