AI in Residence

Summary

Xaira Therapeutics is an innovative biotech startup focused on leveraging AI to transform drug discovery and development. The AI in Residence role is designed for exceptional researchers and engineers to apply advanced AI to real biomedical problems, collaborating closely with teams to design and implement machine learning capabilities that influence therapeutic programs.

Responsibilities

• Develop and advance ML models for biological, preclinical, and translational datasets (e.g., multimodal omics, imaging, text, assay data)
• Design and implement scalable pipelines for data curation, training, evaluation, and inference integrated into discovery workflows
• Own projects end-to-end: problem framing → prototyping → validation → deployment
• Evaluate robustness and reliability (generalization, uncertainty, failure modes), plus interpretability where it supports scientific decision-making
• Contribute technical leadership by proposing new directions, shaping platform capabilities, and raising engineering/research standards through collaboration

Skills

• Technical depth
• Intellectual independence
• Strong research judgment
• Evidence of delivering high-quality work—whether through publications, open-source, or production systems
• Recent MS or PhD graduates (or equivalent research experience) in ML/AI, computational biology, biomedical engineering, or related fields
• Evidence of research excellence through high-quality publications or artifacts. Top venues (e.g., NeurIPS, ICML, ICLR, CVPR, ACL; Nature Methods, Cell Systems) are a plus, but strong preprints, open-source contributions, or shipped systems with demonstrated impact are equally compelling
• Demonstrated ability to lead substantial technical work with originality—new modeling ideas, rigorous experiments, or production-grade systems adopted by others
• Motivation to translate rigorous research into reliable, deployable AI systems that support therapeutic discovery

Qualifications

Must Haves

• Technical depth
• Intellectual independence
• Strong research judgment
• Evidence of delivering high-quality work—whether through publications, open-source, or production systems

Nice to Haves

• Recent MS or PhD graduates (or equivalent research experience) in ML/AI, computational biology, biomedical engineering, or related fields
• Evidence of research excellence through high-quality publications or artifacts. Top venues (e.g., NeurIPS, ICML, ICLR, CVPR, ACL; Nature Methods, Cell Systems) are a plus, but strong preprints, open-source contributions, or shipped systems with demonstrated impact are equally compelling
• Demonstrated ability to lead substantial technical work with originality—new modeling ideas, rigorous experiments, or production-grade systems adopted by others
• Motivation to translate rigorous research into reliable, deployable AI systems that support therapeutic discovery