Job description

About Xaira Therapeutics Xaira is an innovative biotech startup focused on leveraging AI to transform drug discovery and development. The company is leading the development of generative AI models to design protein and antibody therapeutics, enabling the creation of medicines against historically hard-to-drug molecular targets. It is also developing foundation models for biology and disease to enable better target elucidation and patient stratification. Collectively, these technologies aim to continually enable the identification of novel therapies and to improve success in drug development. Xaira is headquartered in the San Francisco Bay Area, Seattle, and London. 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. 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.

Requirements

Must-haves

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.
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.

Nice-to-haves

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.

Application prep

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Keywords to mirror

aimachine learningmlpython

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