Scientist Jobs

About The Position

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).

Nice To Haves

• Exceptional ML candidates willing to develop biological expertise on the job.

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.

Benefits

• Annual bonus
• Equity compensation
• Comprehensive benefits package