AI Scientist, BioMedical AI

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.

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 $150,000 - $240,000 annually; however, the base pay offered may vary depending on the market, job-related knowledge, skills and capabilities, and experience.

Xaira Therapeutics an equal-opportunity employer. We believe that our strength is in our differences. Our goal to build a diverse and inclusive team began on day one, and it will never end.

TO ALL RECRUITMENT AGENCIES: Xaira Therapeutics does not accept agency resumes. Please do not forward resumes to our jobs alias or employees. Xaira Therapeutics is not responsible for any fees related to unsolicited resumes.

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.