AI in Residence, Computational Protein Design

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.

AI in Residence

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.

• 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

You Might Work On

• 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)

What Success Looks Like

• You ship one or more models or pipelines that are used in real discovery workflows.

• Your work improves decision quality (e.g., better prioritization, faster iteration, clearer uncertainty).

• You raise the bar on evaluation rigor and reproducibility (strong baselines, error analysis, reliable metrics)

• You leave behind maintainable systems (tests, documentation, monitoring) that others can build on

We Value

• 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