AI in Residence, Computational Protein Design

Xaira Therapeutics • Seattle, WA

18h • $10,000 - $15,000 • Hybrid

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
• Strong research judgment: choosing the right problems and knowing what “good evidence” looks like.
• Rigor: careful experimental design, ablations, error analysis, and honest reporting.
• Systems thinking: reliability, scalability, and maintainability—not just prototypes.
• Clear communication: writing, documentation, and sharing decisions/assumptions.
• Collaborative execution with scientific and engineering partners

Nice To Haves

• Foundation / representation models for protein/antibody structure, sequence and property modeling and prediction
• Methods for small, biased, noisy datasets; distribution shift; and uncertainty estimation.
• ML systems for experimental prioritization, assay interpretation, or translational signal discovery
• Evaluation frameworks and benchmarks tailored to discovery decision-making. Tooling that makes models usable by scientists (interfaces, automation, monitoring)

Responsibilities

• Develop and advance ML models for protein and antibody design using biophysical data, affinity data, library display data, protein structure datasets, and protein sequence datasets
• 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

Benefits

• Dedicated technical mentor, plus structured feedback from senior AI, engineering, and scientific leadership
• Support for publications and conference presentations when appropriate
• Monthly compensation range of $10,000–$15,000, depending on experience and qualifications. Open to higher compensation for candidates with exceptional experience or impact.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level