Research Scientist, Virtual Cell Modelling & Perturbative Biology Foundation Models

Please note: Our offices will be closed for our annual Summer Break from Monday, June 29, 2026, through Friday, July 3, 2026. Our response to your application will be delayed. 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 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, a