Research Scientist, Virtual Cell Modelling & Perturbative Biology Foundation Models

with significant academic or industry research experience in machine learning applied to drug discovery, life sciences or other real-world scientific or engineering problems.

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.

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.

, 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.

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

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.

Generative Modeling: