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. We are seeking a Research Scientist with strong ML research and engineering skills, and genuine curiosity for biology, to join a multidisciplinary team of ML researchers, engineers, and computational biologists working toward a shared goal: building virtual cells that transform how medicines are discovered.
Key Responsibilities

• 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.
• Biological Grounding: Work closely with colleagues to ensure model predictions are interpretable, trustworthy, actionable, and grounded in real experimental outcomes.
• 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.
• Open Science & Collaboration: Publish findings in top-tier venues (e.g., NeurIPS, ICML, Nature, Science, Cell) and contribute to the broader scientific community.

What We're Looking For

We prioritize scientific depth in both ML and biology, but will consider exceptional ML candidates willing to develop biological expertise on the job. A successful candidate will have most of the following:

• 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); experience with biological data is a plus.
• Scientific knowledge of biology or chemistry, with familiarity with perturbational / interventional experimental paradigms (e.g., chemical or genetic screens, transcriptomics, high-content imaging).
• 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; Python proficiency required, experience with compiled languages a plus.
• 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.
• Leadership and communication skills: including an authorship record in peer-reviewed conferences (e.g., NeurIPS, ICML, ICLR) or journals (e.g., Nature, Science, Cell).

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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