Valence Labs

Research Scientist, Virtual Cell Modelling & Perturbative Biology Foundation Models

Montréal, Quebec

Sponsorship not specifiedDetected 14 days ago

PythonMachine LearningResearchLeadershipCommunicationCollaboration

About the role

You will be joining a research program building multimodal foundation models to predict cellular responses to chemical and genetic perturbations across petabyte-scale omics and imaging data.
The work spans generative and distributional modeling, representation learning for molecules and genes/proteins, and the design of biologically grounded evaluation frameworks.
The goal is to close critical gaps in the pre-clinical pipeline, replacing or augmenting wet-lab perturbation screens with in silico predictions that are reliable enough to drive drug discovery decisions.

Generative Modeling: Research and develop generative and distributional models (e.g., flow matching, diffusion models) to predict high-dimensional cellular responses.
Scalable ML Engineering: Build and maintain ML systems capable of processing massive multiomics datasets on high-performance compute clusters.
Evaluation Frameworks: Help design and implement rigorous evaluation metrics that test generalization across for cellular context, unseen perturbations and covariates, going beyond IID performance to reflect real deployment conditions.
Impactful research track record, including developing ML models for complex real-world data, proposing new training or evaluation approaches, or applying generative methods to scientific problems, particularly in biology or life sciences.
Strong technical and engineering skills, including the ability to rapidly prototype and scale ML models, manage large codebases, and maintain reproducible research pipelines
We lead high-impact research programs designed to materially expand Recursion's ability to discover and develop medicines for complex diseases.

Scientific knowledge of biology or chemistry, with familiarity with perturbational / interventional experimental paradigms (e.g., chemical or genetic screens, transcriptomics, high-content imaging).
Cross-functional comfort, with the ability to work effectively across disciplines (e.g with dry and wet-lab scientists) to ensure models address real scientific questions.
Compensation packages are competitive and commensurate with the skills and level of experience required for this role.
packages are competitive and commensurate with the skills and level of experience required for this role.

including an authorship record in peer-reviewed conferences (e.g., NeurIPS, ICML, ICLR) or journals (e.g., Nature, Science, Cell).
Working Location &

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

Our work is driven by optimism, purpose, and a shared vision for a healthier tomorrow.
Our teams are based in London and Montreal, with deep ties to Mila, the world's largest deep-learning research institute.
PhD (or equivalent) with significant academic or industry research experience in machine learning applied to drug discovery, life sciences or other real-world scientific or engineering problems.
Strong background in generative modeling and representation learning, with experience applying these to high-dimensional scientific data (e.g., images, count matrices, graphs)

This listing is sourced directly from Valence Labs's careers page and normalized into a canonical job model.