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About the Role

Join a research program building multimodal foundation models to predict cellular responses to chemical and genetic perturbations across petabyte-scale omics and imaging data. The goal is to replace or augment wet-lab perturbation screens with in silico predictions that drive drug discovery decisions. We seek an ML researcher with curiosity for biology to join a multidisciplinary team.

Responsibilities

• Research and develop generative and distributional models for predicting high-dimensional cellular responses.
• Build and maintain ML systems for processing massive multiomics datasets on high-performance compute clusters.
• Ensure model predictions are interpretable, trustworthy, actionable, and grounded in experimental outcomes.
• Design and implement rigorous evaluation metrics that test generalization across cellular contexts and unseen perturbations.
• Publish findings in top-tier venues and contribute to the broader scientific community.

Requirements

• 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.
• Scientific knowledge of biology or chemistry, with familiarity with perturbational / interventional experimental paradigms.
• 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.
• 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.
• Cross-functional comfort, with the ability to work effectively across disciplines.
• Leadership and communication skills, including an authorship record in peer-reviewed conferences or journals.

Skills

• Machine learning
• Generative modeling
• Representation learning
• High-dimensional data analysis
• Python
• ML systems engineering
• Biological data analysis
• Scientific communication

Location

• Montreal, Quebec, Canada

Work Type

• Office-based
• Hybrid

Experience Level

• PhD with significant research experience

Education Level

• PhD or equivalent

Salary/Compensations

• Competitive compensation commensurate with skills and experience
• Annual bonus
• Equity compensation

Benefits

• Comprehensive benefits package

About the Company

• Valence Labs is Recursion’s frontier AI research engine, leading high-impact research programs to expand Recursion’s ability to discover and develop medicines for complex diseases.
• The team balances near-term pragmatism with a long-term view, incubating, designing, and productizing approaches that will define the future of drug discovery.
• Work is driven by optimism, purpose, and a shared vision for a healthier tomorrow.
• Publish in top journals and conferences, contribute to open science, and engage with ML-for-drug-discovery research communities.
• Teams are based in London and Montreal, with deep ties to Mila, the world’s largest deep-learning research institute.