AI Scientist: Single-Cell & Multimodal Foundations
Xaira Therapeutics
South San Francisco (CA)
On-site
USD 150,000 - 240,000
Full time
14 days+
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Benefits offered by this job
Competitive package including bonus and equity
Job summary
An innovative biotech startup in South San Francisco is seeking an AI Scientist to develop and apply machine learning models to analyze single-cell datasets. The ideal candidate will hold a PhD in a relevant discipline and possess strong expertise in single-cell data analysis and foundation models. Responsibilities include designing scalable ML architectures and collaborating with biologists to drive discovery. The compensation package includes a competitive salary ranging from $150,000 to $240,000 annually based on experience.
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.
Responsibilities
Design, train, and refine foundation models tailored to perturb‑seq and other single‑cell datasets.
Build pipelines for preprocessing and integration of large‑scale single‑cell data.
Apply ML/AI approaches to model cellular responses and identify insights.
Skills
Single-cell data analysis
Foundation models
Python programming
Machine Learning frameworks
Scalable ML methods
Education
PhD in Computational Biology or related discipline
Tools
PyTorch
TensorFlow
Scanpy
Seurat
Job description
An innovative biotech startup in South San Francisco is seeking an AI Scientist to develop and apply machine learning models to analyze single-cell datasets. The ideal candidate will hold a PhD in a relevant discipline and possess strong expertise in single-cell data analysis and foundation models. Responsibilities include designing scalable ML architectures and collaborating with biologists to drive discovery. The compensation package includes a competitive salary ranging from $150,000 to $240,000 annually based on experience.