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Physics Informed Machine Learning Jobs in Quebec

SAP

Senior AI Machine Learning Engineer

Montreal, QC

Required Qualifications · PhD or MS in Computer Science, Machine Learning, Applied Mathematics, Physics, or a related field, with substantial applied experience. · 5+ years building and delivering ...

Valence Labs

ML Intern, Research

Montreal, QC · On-site

In addition, we highly value proficiency with state-of-the-art machine learning algorithms and ... Scientific knowledge of biology, chemistry, or physics along with previous experience working in a ...

Targeted Talent

Data Scientist

Montreal, QC · On-site

$80K - $100K/yr

Physics, Computer Science, Stats) * 1-2 years' experience developing solutions and working with ... Previous experience with Machine Learning, Data Science and solving problems at scale Perks:

Targeted Talent

Data Scientist

Montreal, QC

$80K - $100K/yr

Physics, Computer Science, Stats) * 1-2 years' experience developing solutions and working with ... Previous experience with Machine Learning, Data Science and solving problems at scale Perks:

TouchTunes

Senior Data Scientist

Montreal, QC

Apply statistical or machine learning knowledge to specific business problems and data. * Develop ... Staying informed about developments in Data Science and adjacent fields to ensure that outputs are ...

Td

Sr. Full Stack Data Science Engineer

Montreal, QC · On-site

CA$154K - CA$199.50K/yr

... that drive informed decisionmaking. * ML/AI Lifecycle Familiarity : Experience working with ... Solid knowledge of applied Machine Learning, Deep Learning, Large Language Models * Solid cloud ...

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What is a Physics Informed Machine Learning job?

A Physics Informed Machine Learning (PIML) job involves developing AI models that integrate physics-based principles to improve accuracy, interpretability, and generalization. Professionals in this role use machine learning techniques alongside domain knowledge in physics, engineering, or applied sciences to solve complex problems in areas like fluid dynamics, materials science, and climate modeling. Responsibilities often include designing algorithms, implementing simulations, and validating results against experimental or real-world data. Employers typically seek expertise in deep learning, numerical methods, and programming languages like Python.

What are the key skills and qualifications needed to thrive in the Physics Informed Machine Learning position, and why are they important?

To thrive in Physics Informed Machine Learning, you need a solid background in physics, strong mathematical and statistical skills, and experience with machine learning algorithms, typically supported by an advanced degree in a relevant field. Proficiency with programming languages like Python, frameworks such as TensorFlow or PyTorch, and familiarity with numerical simulation tools are commonly required. Effective problem-solving, clear communication, and the ability to collaborate with interdisciplinary teams make a significant impact in this role. These capabilities are essential for developing robust, interpretable machine learning models that leverage physical laws to solve complex, real-world problems.

What are the typical challenges faced by professionals working in Physics Informed Machine Learning roles?

Professionals in Physics Informed Machine Learning often encounter challenges integrating complex physical theories with advanced machine learning models, requiring deep domain knowledge and strong technical skills. Balancing model accuracy with computational efficiency and ensuring that models are both interpretable and generalizable can be demanding. Collaboration with domain experts, data scientists, and engineers is common, as projects often span multiple disciplines. Successfully navigating these challenges provides valuable experience and is highly regarded, often leading to further career advancement in research, engineering, or leadership positions.
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Physics Informed Machine Learning jobs near you
Infographic showing various Physics Informed Machine Learning job openings in Quebec as of May 2026, with employment types broken down into 100% Full Time. Highlights an 80% In-person, and 20% Hybrid job distribution.

Research Scientist, Next-Generation Structural Biology & Atomistic Modeling

Valence Labs

Montreal, QC • Hybrid

Other

Posted 10 days ago

Job description

About Valence Labs

Valence Labs is Recursion's frontier AI research engine. We lead high-impact research programs designed to materially expand Recursion's ability to discover and develop medicines for complex diseases.

Our team balances near-term pragmatism with a long-term view of where the field is heading in the next 3-5 years, incubating, designing, and productizing the approaches we believe will define the future of drug discovery. Our work is driven by optimism, purpose, and a shared vision for a healthier tomorrow. We publish in top journals and conferences, contribute to open science, and engage with some of the world's most active ML-for-drug-discovery research communities. Our teams are based in London and Montreal, with deep ties to Mila, the world's largest deep-learning research institute.

About The Role

We are seeking a Research Scientist with a hybrid research-engineering mindset to join our team. In this role, you will be at the forefront of developing generative architectures and foundation models that ground machine learning in real-world physical and biological discovery. You will focus on accelerating and improving the accuracy of molecular design and structural biology workflows-specifically targeting the intersection of physics-informed frameworks and data-driven ML to solve complex protein-ligand interaction challenges.

Key Responsibilities
  • Model Innovation: Research and develop state-of-the-art architectures (e.g., flow matching, diffusion models, geometric deep learning) tailored to modeling protein-ligand interactions.
  • Physics-ML Integration: Develop hybrid approaches that integrate co-folding, molecular dynamics (MD), and experimental potency data to achieve high-resolution accuracy on novel targets.
  • Scalable Engineering: Build and maintain ML systems capable of processing massive datasets, such as protein-ligand simulations, on high-performance compute clusters (BioHive).
  • Biological Grounding: Ensure ML predictions are biologically trustworthy and actionable by collaborating closely with drug discovery teams to reduce cycle periods and dead ends in lead optimization.
  • Open Science & Collaboration: Publish findings in top-tier venues (e.g., NeurIPS, ICML, Nature, JACS) and contribute to the broader scientific community.
A successful candidate will have most of the following:
  • PhD (or equivalent) with significant academic or industry research experience in machine learning applied to structural biology, atomistic modeling, or physical simulation.
  • Scientific knowledge of physics and chemistry, with a deep understanding of physical constraints and invariances in molecular systems.
  • Impactful research track record, including experience with equivariant models, generative modeling of molecular systems, or replacing traditional physics workflows (like ABFE) with ML-driven alternatives.
  • Strong technical and engineering skills, including proficiency in Python and the ability to build scalable, reproducible experiment pipelines.
  • Interdisciplinary empathy, with a proven ability to work effectively with medicinal chemists and biophysicists to ensure models solve real-world drug discovery problems.
  • Leadership and communication skills, including the ability to explain complex ideas clearly to both technical and non-technical stakeholders.
Working Location & Compensation:

This is an office-based, hybrid position at either of our offices located in Montreal, Quebec, Canada. Employees are expected to work in the office at least 50% of the time.

Compensation packages are competitive and commensurate with the skills and level of experience required for this role. In addition to base salary you will also be eligible for an annual bonus and equity compensation, as well as a comprehensive benefits package. 

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