AI in Residence
About The Position
AI in Residence is a highly selective role at the intersection of frontier machine learning and drug discovery. Designed as an industry alternative to a traditional postdoctoral position, the program is for exceptional researchers and engineers who want to apply advanced AI to real biomedical problems end to end, from data to deployed systems. Residents join a small cohort working on high-impact AI efforts across Xaira. You’ll collaborate closely with AI scientists, research engineers, and drug discovery teams to design, build, and ship machine learning capabilities that directly influence therapeutic programs. This is hands-on, system-level work with real scientific consequence. We’re looking for candidates with technical depth, intellectual independence, strong research judgment, and evidence of delivering high-quality work—whether through publications, open-source, or production systems.
Requirements
- 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
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
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What This Job Offers
Career Level
Entry Level
