Senior Scientist, Computational Biology

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.

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.

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.

This will be an on‑site role in South San Francisco.

• 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
• 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
• Build and maintain scalable, production‑grade bioinformatics pipelines, with an emphasis on reproducibility, modularity, and performance at the scale of large perturbation screens
• 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 multimodal analyses
• Partner closely with AI/ML scientists to ensure data products meet modeling requirements, and collaborate with wet‑lab scientists to translate experimental design into optimal computational workflows
• Manage code repositories, maintain versioned and documented workflows, and uphold computational environment standards that ensure long‑term pipeline reliability
• Contribute to scientific strategy, publications, and patents while mentoring and providing scientific guidance to researchers within the organization

• Ph.D. in Computational Biology, Bioinformatics, Genomics, or a related quantitative field, with 5+ years of relevant industry and/or applied research experience (candidates with fewer years are also encouraged to apply and job level can be adjusted as appropriate)
• Deep expertise in single cell RNA sequencing analysis with substantial hands‑on experience in Perturb‑seq or CRISPR‑based perturbation screen data, including thorough familiarity with the full processing stack from raw reads to interpretable outputs
• Demonstrated ability to integrate heterogeneous datasets from multiple sources, with a strong command of batch correction, normalization, and quality control strategies tailored to large‑scale perturbation experiments
• Experience building and maintaining production bioinformatics workflows (e.g. Nextflow), comfort with containerized environments (Docker) and cloud computing platforms (AWS)
• Strong software engineering fundamentals: proficiency in Python (numpy, pandas, scanpy, scikit‑learn), fluency in Unix environments, and experience with version control and collaborative software development via Git Hub.
• Working knowledge of statistical and machine learning methods applied to…