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Scientific Machine Learning Jobs in Quebec (NOW HIRING)

This role is primarily about scientific problem solving and applied machine learning, not ML infrastructure. You will work closely with the ML lead, who will support integration and productionization ...

We are seeking a Machine Learning (ML) Manager to join our growing team dedicated to a novel AI ... You will bridge the gap between cutting-edge scientific theory and large-scale engineering ...

We're looking for a highly motivated Applied Machine Learning Scientist II to join our AI2 team. In this role, you'll apply expertise across the end-to-end AI and machine learning lifecycle ...

Senior Machine Learning Engineer

Montreal, QC · On-site

CA$147K - CA$220K/yr

D.) in Computer Science, Machine Learning, Robotics, or a related field-or equivalent practical experience. * 5+ years of hands-on experience developing ML systems in production, ideally within real ...

About the role Intact is looking for a Data Scientist to turn complex data into practical solutions ... Use machine learning and advanced statistical methods to identify trends and patterns in complex ...

About the role Intact is looking for a Data Scientist to turn complex data into practical solutions ... Utilize machine learning and advanced statistical methods to identify trends and patterns in ...

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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 Quebec?

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

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

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

Infographic showing various Scientific Machine Learning job openings in Quebec as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 20% Part Time, and 1% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution.

Machine Learning Scientist

Eli Health

Montreal, QC • On-site

Full-time

Medical, Dental, Vision

Re-posted 12 days ago


Job description

About us

Eli Health is making continuous hormone monitoring possible so users can support their daily and long-term health. No more waiting days to track key biomarkers—get results you can use within minutes. Eli’s flagship product, Hormometer®, is the first instant hormone monitoring platform to deliver results from saliva to mobile app—anytime, anywhere.

Developed over six years of R&D, over 2,000 product iterations, and backed by a dozen patent-pending innovations, Eli’s award-winning platform turns hormones into measurable signals you can track and improve. Just as the thermometer and glucometer have transformed health for millions, Eli’s platform is poised to be the next major evolution in tracking changes in stress, endurance, sleep, and more.

About the role

Eli is looking for an on-site Applied Machine Learning Scientist to join our ML team and help solve challenging real-world problems involving imperfect, noisy, and complex datasets.

This role is primarily about scientific problem solving and applied machine learning, not ML infrastructure. You will work closely with the ML lead, who will support integration and productionization of your work

Where you'll spend the first half of your time

You’ll work on open-ended scientific and machine-learning problems, turning imperfect real-world data into robust, reproducible analyses and models. The results of these analyses will have to be communicated effectively to the broader team. The focus is on understanding the problem deeply, choosing the right methods, and validating that improvements are real and generalizable.

  • Explore datasets and understand the underlying data-generating processes.

  • Develop, evaluate, and improve machine learning, signal processing, and statistical models.

  • Perform feature engineering, model selection, validation, and error analysis.

  • Identify issues such as confounding, data leakage, measurement variability, and distribution shift.

  • Design experiments and analyses to resolve uncertainty and guide modelling decisions.

  • Investigate new modelling approaches, including classical ML and deep learning (when appropriate).

  • Produce clear, reproducible Python code that isn’t limited to notebooks, and communicate findings to technical and non-technical stakeholders.


Where you'll spend the other half

You’ll stay closely connected to how models behave in the real world. You’ll investigate production performance, diagnose failures and unexpected behavior, and use those observations to drive new analyses, experiments, and model improvements.

  • Develop analyses and models with the expectation that it will be deployed into production.

  • Investigate model performance and failures using real-world production data.

  • Perform ongoing error analysis, diagnostics, and root-cause investigations.

  • Identify distribution shifts, edge cases, systematic biases, and degradation in model performance.

  • Translate production observations into experiments, improvements, or data-collection strategies.

What we are looking for

Someone with a bachelor's degree (Master’s or PhD preferred) in Engineering, Computer Science, Data Science, Mathematics, or a related field who possesses a minimum of 5 years of professional experience excluding internships. Additional capabilities include:

  • Strong foundation in machine learning fundamentals, statistics, and experimental reasoning.

  • Strong Python skills and experience with common ML/data science libraries.

  • Ability to work independently on ambiguous problems and determine what questions need to be answered.

  • Good understanding of model validation, uncertainty, bias/variance, and generalization.

  • Ability to distinguish between improvements that are statistically or scientifically meaningful and those that simply improve a metric.

Above all, we are looking for someone who is particularly good at answering the following question:

Given the data we have and the problem we are trying to solve, what can we conclude with confidence, what remains uncertain, and what should we do next?

Why you’ll love working at Eli

  • You’ll work with a group of talented and mission-driven people eager to improve lifelong health at scale.

  • You’ll be part of the core team developing and commercializing the first product that monitors hormonal data daily and over a lifetime.

  • You’ll join the early-stage startup phase and have a wide-reaching impact in a constantly evolving, fast-paced environment.

  • You’ll be part of a small (<25 people), high-performing, and diverse team where everything you do results in tangible impact and shapes the company’s trajectory.

  • You’ll be in an environment where people drive their own work, think creatively about open-ended problems, and solve them proactively.

  • You’ll get health insurance (medical, dental, vision, and more) to ensure you and your family stay physically and mentally at your best.

  • You’ll have flexibility over your schedule and vacations. We seek to hire great people, then give them the autonomy and space they need to achieve their goals.

  • Although our office and R&D facilities are in Montreal, we have a distributed team. We prioritize asynchronous workflows and minimize meetings to focus on the work itself.

  • You and your +1 will have unlimited free access to the Bota Bota spa in Montreal to recharge.

How to apply

Sounds like you? Please apply using this link:https://jobs.ashbyhq.com/eli. Please note that we will not review applications made through other platforms. We look forward to learning about you!