PhD or equivalent in Computational Biology, Computer Science, Bioinformatics, or related discipline.
Strong expertise in single-cell data analysis, particularly with perturb-seq.
Hands-on experience with large-scale self-supervised learning models (e.g., transformers, VAEs).
Proficient in Python, along with ML frameworks like PyTorch and TensorFlow.
Experience in developing scalable ML methods for large biological datasets.
Responsibilities
Design, train, and refine foundation models for perturb-seq and single-cell datasets.
Build data pipelines for preprocessing and normalization of large-scale single-cell data.
Apply ML methods to model cellular responses and uncover causal biological insights.
Collaborate with experimental teams to align computation with experimental goals.
Develop approaches for multimodal integration across different omics layers.
Publish and present research findings in leading scientific journals and conferences.
Benefits
Flexible and friendly work environment.
Opportunities for career development and long-term growth.
Diversity and inclusion commitment within the team.
Full Job Description
About the Role We are looking for an AI Scientist to join our team at the intersection of machine learning and single-cell biology. This role will focus on developing and applying foundation models to large-scale perturb-seq and single-cell datasets to uncover new biological mechanisms and therapeutic opportunities. You will help design scalable ML architectures that can extract insight from high-dimensional perturbation data and integrate across multi-omic modalities.
Key Responsibilities
Design, train, and refine foundation models tailored to perturb-seq and other single-cell datasets. Build pipelines for preprocessing, normalization, and integration of large-scale single-cell and perturbation data.
Apply ML/AI approaches to model cellular responses to perturbations and identify causal biological insights.
Collaborate with experimental biologists to align computational methods with experimental design and discovery goals.
Develop multimodal integration approaches across transcriptomic, epigenomic, and proteomic single-cell readouts.
Publish methods and results in top scientific journals and present at conferences.
Qualifications
PhD or equivalent in Computational Biology, Computer Science, Bioinformatics, or related discipline.
Strong expertise in single-cell data analysis, particularly perturb-seq
Hands-on experience with foundation models or large-scale self-supervised learning (e.g., transformers, variational autoencoders).
Strong coding skills in Python, ML frameworks (PyTorch/TensorFlow), and single-cell analysis tools (Scanpy, Seurat).
Experience in developing scalable ML methods for large biological datasets.
Preferred
Background in causal inference, generative models, or representation learning for biological systems.
Familiarity with multi-omics integration and cross-modal foundation models.
Track record of impactful publications in computational biology, machine learning, or related fields.
Compensation
We offer a competitive compensation and benefits package, seeking to provide an open, flexible, and friendly work environment to empower employees and provide them with a platform to develop their long-term careers. A Summary of Benefits is available for all applicants. We offer a competitive package that includes base salary, bonus, and equity. The base pay range for this position is expected to be $212,000 - $265,000 annually; however, the base pay offered may vary depending on the market, job-related knowledge, skills and capabilities, and experience.
