Senior Scientist, Computational Biology

<div class="content-intro"><p><strong>About Xaira Therapeutics</strong></p> <p>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.</p></div><p><strong>ABOUT THE ROLE</strong></p> <p>Xaira is seeking a highly motivated and experienced Senior Scientist to join our Computational Biology team with a focus on supporting large-scale single-cell perturbation screens. The ideal candidate will bring deep expertise in single-cell RNA sequencing, Perturb-seq, and a proven track record of processing and integrating large-scale datasets from multiple experimental sources.</p> <p>This position offers the opportunity to contribute to cutting-edge efforts at the intersection of functional genomics, machine learning, and disease biology. You will work closely with wet-lab scientists, AI/ML scientists, automation engineers, and disease biology teams to develop scalable perturbation workflows, implement new technologies, and drive high-quality data generation and experimental innovation across multiple therapeutic areas.</p> <p>This will be an on-site role in South San Francisco.</p> <p>&nbsp;</p> <p><strong>KEY RESPONSIBILITIES</strong></p> <ul> <li>Own end-to-end computational analysis of Perturb-seq datasets, spanning quality control, biological interpretation, and translation of large-scale single-cell data into actionable insights for therapeutic discovery and AI/ML modeling</li> <li>Design and implement standardized analytical frameworks for integrating Perturb-seq data across diverse experimental contexts, such as cell types, library designs, and sequencing platforms, to enable reliable cross-source analysis</li> <li>Build and maintain scalable, production-grade bioinformatics pipelines, with an emphasis on reproducibility, modularity, and performance at the scale of large perturbation screens</li> <li>Integrate Perturb-seq data with complementary omics modalities, including CITE-seq, ATAC-seq, spatial transcriptomics, as well as external reference datasets, to enrich biological context and support multi-modal analyses</li> <li>Partner closely with AI/ML scientists to ensure data products meet modeling requirements, and collaborate with wet-lab scientists to