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Scientific Machine Learning Jobs in Ontario (NOW HIRING)

Td

Applied Machine Learning Scientist I

Toronto, ON

CA$105K - CA$125K/yr

Additional We're looking for a highly motivated Applied Machine Learning Scientist to join our AI2 team. In this role, you'll drive the development and deployment of ML solutions that power data ...

Td

Senior Machine Learning Engineer

Toronto, ON

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 ...

Td

Applied Machine Learning Scientist II

Toronto, ON

CA$125K - CA$154K/yr

We're looking for a highly motivated Applied Machine Learning Scientist to join our AI2 team. In this role, you'll drive the development and deployment of ML solutions that power data-driven decision ...

Autodesk

A degree in a related field (Data Science, Computer Science, Statistics or a quantitative-related ... Expertise with Machine Learning, Deep Learning and statistical modeling tools and libraries such as ...

Adobe

Machine Learning Engineer

Toronto, ON · On-site

Experience fine-tuning LLMs for specific learning tasks Experience deploying full-stack features to ... science, statistics, engineering, or other relevant subject area highly preferred 3+ years of ...

Td

Applied Machine Learning Scientist II

Toronto, ON · On-site

CA$125K - CA$154K/yr

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

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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:

Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in Ontario as of August 2026, with employment types broken down into 1% As Needed, 70% Full Time, 28% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Applied Machine Learning Scientist I

Td

Toronto, ON

CA$105K - CA$125K/yr

Full-time

Re-posted 21 hours ago

Job description

Work Location:

Toronto, Ontario, Canada

Hours:

37.5

Line of Business:

Analytics, Insights, & Artificial Intelligence

Pay Details:

$105,500 - $125,000 CAD

TD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience at TD. Our compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs.

As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role.

Job Description:

Additional Job Description

We're looking for a highly motivated Applied Machine Learning Scientist to join our AI2 team. In this role, you'll drive the development and deployment of ML solutions that power data-driven decision-making across our Canadian Personal Banking division. You'll work closely with various teams to bring AI capabilities to life and deliver measurable business impact.

This is a unique opportunity work on high-impact initiatives in a fast-growing function. If you're passionate about solving real-world problems with machine learning and want to make a tangible difference at scale, we'd love to hear from you.

What You'll Do

  • Develop, deploy, and maintain Predictive and Generative AI models for use cases such as Agentic AI, Chatbots, Pricing, and Anomaly Detection, Forecasting

  • Design and implement clean, modular, and reusable ML codebases using object-oriented programming (OOP) principles and best coding practices

  • Translate business problems into analytical frameworks and collaborate with cross-functional teams to define success metrics and solution approaches

  • Build production-ready ML pipelines, ensuring robustness, scalability, and maintainability

  • Conduct rigorous model evaluation, documentation, A/B testing, and monitoring to ensure model performance, fairness, and stability in production

  • Communicate complex technical results to non-technical stakeholders and provide actionable insights

  • Stay current with ML research, GenAI advancements, and software engineering best practices to bring innovative approaches into production

What You Bring

  • 2+ years of experience applying machine learning to real-world business problems

  • Strong proficiency in Python for ML modeling and implementation

  • Experience building maintainable, well-tested, and production-quality ML code

  • Proficiency with key ML libraries and frameworks

  • Experience working with structured and unstructured data, feature engineering, and model interpretability techniques

  • Exposure to GenAI or large language models and their practical applications

  • Hands-on experience with model deployment, monitoring, and lifecycle management in production environments

  • Strong problem-solving skills and a track record of delivering results in cross-functional teams

  • Undergraduate degree required; advanced technical degree in a STEM field preferred

Nice to Have

  • Experience working in financial services or regulated environments

  • Familiarity with causal inference, anomaly detection, or agent-based systems

  • Experience applying software engineering practices such as code reviews, version control, testing, and documentation in ML projects

Who We Are:

TD is one of the world's leading global financial institutions and is the fifth largest bank in North America by branches/stores. Every day, we strive to make every interaction, product, and experience remarkably human and refreshingly simple for over 27 million households and businesses in Canada, the United States and around the world. More than 95,000 TD colleagues bring their skills, talent, and creativity to foster deeper relationships, ensure disciplined execution, and build a simpler, faster banking experience. TD is deeply committed to being a leader in client experience, that is why we believe that all colleagues, no matter where they work, are client facing. Together, we are reimagining what banking can be for our clients, colleagues and communities.

Our Total Rewards Package
Our Total Rewards package reflects the investments we make in our colleagues to help them and their families achieve their financial, physical, and mental well-being goals. Total Rewards at TD includes a base salary, variable compensation, and several other key plans such as health and well-being benefits, savings and retirement programs, paid time off, banking benefits and discounts, career development, and reward and recognition programs. Learn more

Additional Information:
We're delighted that you're considering building a career with TD. Through regular development conversations, training programs, and a competitive benefits plan, we're committed to providing the support our colleagues need to thrive both at work and at home.

Please be advised that this job opportunity is subject to provincial regulation for employment purposes. It is imperative to acknowledge that each province or territory within the jurisdiction of Canada may have its own set of regulations, requirements.


Colleague Development

If you're interested in a specific career path or are looking to build certain skills, we want to help you succeed. You'll have regular career, development, and performance conversations with your manager, as well as access to an online learning platform and a variety of mentoring programs to help you unlock future opportunities.

If you're passionate about helping clients and building deep, lasting relationships, TD offers diverse career paths where you can grow your expertise and make a meaningful impact.

We're committed to your success and foster a respectful workplace where diverse perspectives are valued, everyone has fair opportunities to grow, and you can unlock your full potential to achieve your career goals. Here at TD, we hire and develop the best.

Training & Onboarding
We will provide training and onboarding sessions to ensure that you've got everything you need to succeed in your new role.

Interview Process
We'll reach out to candidates of interest to schedule an interview. We do our best to communicate outcomes to all applicants by email or phone call.


Accommodation
Your accessibility is important to us. Please let us know if you'd like accommodations (including accessible meeting rooms, captioning for virtual interviews, etc.) to help us remove barriers so that you can participate throughout the interview process.
We look forward to hearing from you!

Language Requirement (Quebec only):

Sans Objet

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