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VL
Research Scientist, Virtual Cell Modelling, Perturbative Biology
Posted 2 months ago
About the role
- Research Scientist at Valence Labs developing ML models for predicting cellular responses in drug discovery. Building generative models based on massive multiomics datasets with collaborative research.
Responsibilities
- Research and develop generative and distributional models (e.g., flow matching, diffusion models) to predict high-dimensional cellular responses.
- Build and maintain ML systems capable of processing massive multiomics datasets on high-performance compute clusters.
- Work closely with colleagues to ensure model predictions are interpretable, trustworthy, actionable, and grounded in real experimental outcomes.
- 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.
- Publish findings in top-tier venues (e.g., NeurIPS, ICML, Nature, Science, Cell) 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 (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).
Benefits
- comprehensive benefits package
- annual bonus
- equity compensation
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