AI Scientist - Biomedical Multimodal Modeling

South San Francisco, CAfull time

On Site

$170k–$240k

H1B: no

Xairatherapeutics The AI Scientist at Xaira Therapeutics develops and applies foundation models to large-scale single-cell and perturb-seq datasets to uncover biological mechanisms and therapeutic opportunities. This role involves designing scalable ML architectures and collaborating with experimental biologists to align computational methods with discovery goals.

Responsibilities

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.
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.

Qualification

Strong coding skills in PythonBackground in causal inference

Required

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.

Benefits

Equity/RSU

Company

Xairatherapeutics

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Source: greenhouse

Active within the past hour

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PythonPyTorchTensorFlowScanpySeuratMachine learning

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