Scientist II, Display Technologies

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. About the Role We are looking for an accomplished protein biochemist to own phage display campaigns end-to-end within our Display Technologies group. You bring deep hands-on display expertise and the judgment to design selection strategies, troubleshoot challenging targets, and turn campaigns into high-quality datasets that feed AI antibody design. You take pride in rigorous bench execution, you think critically about experimental design, and you are comfortable working against difficult target classes—including membrane proteins presented in cell-based expression systems or recombinant formats. Beyond your own bench work, you will help raise the level of the group: mentoring associate scientists, setting technical standards, and partnering with computational and engineering colleagues to scale data generation. You thrive in a fast-paced, collaborative environment where pushing the boundaries of what is possible is the norm. Responsibilities Independently design, execute, and own phage display campaigns from library through panning and hit validation, across both soluble antigens and membrane-protein targets (e.g., GPCRs) presented in cell-based expression systems or recombinant formats. Develop and optimize selection strategies for difficult target classes, including troubleshooting antigen production, presentation, and quality control. Design and build display libraries, applying strong molecular biology fundamentals (cloning, mutagenesis, assembly) to generate high-quality, diverse repertoires. Design and run clonal-level affinity and functional characterization—including flow cytometry (FACS), ELISA, and SPR—and help adapt these assays to higher-throughput and automated formats. Partner with computational and ML colleagues to generate, structure, and quality-control data at scale for model training and evaluation. Mentor associate scientists on display workflows, experimental design, and lab best practices; contribute to technical standards within the group. Contribute to and lead the preparation of technical reports, presentations, and documentation of methods and results. Qualifications PhD in Molecular Biology, Biochemistry, Immunology, Protein Engineering, or a related field, with 2–4 years of relevant industry experience. Significant hands-on expertise with antibody/protein discovery by phage display, including library design, build, panning/selection, and hit validation. Strong molecular biology skills for library construction, including cloning, mutagenesis, and assembly, with the rigor to build high-quality, diverse libraries. Hands-on flow cytometry (FACS) experience is required, including data interpretation and gating strategy. Demonstrated ability to run campaigns independently and troubleshoot challenging targets; experience with membrane proteins (e.g., GPCRs) in cell-based expression systems or recombinant formats is strongly preferred. Strong working knowledge of antibody binding and characterization assays (e.g., ELISA) and proficiency in core molecular and cell biology techniques, including mammalian cell culture. Excellent analytical and problem-solving skills, with meticulous attention to detail and a rigorous, organized approach to experimental design and documentation. Strong written and verbal communication skills, with the ability to present complex scientific concepts to both technical and non-technical audiences. Ability to work effectively in a collaborative, multidisciplinary team, with a track record of fostering knowledge-sharing and supporting more junior colleagues. Nice to Have Experience with label-free affinity characterization (SPR, BLI/Octet). Experience with alternative display formats (yeast, mammalian, or ribosome/mRNA display). Experience with lab automation (e.g., Hamilton, Tecan, or similar liquid handling). Working knowledge of scripting for analysis of large datasets (e.g., Python or R). Compensation Annual We offer a competitive compensation and benefits package, seeking to provide an open, flexible, and friendly work environment to empower employees and provide them with a platform to develop their long-term careers. A Summary of Benefits is available for all applicants. We offer a competitive package that includes base salary, bonus, and