We are hiring for a Scientist II, Antibody Engineering & Characterization to join our growing team. This role will drive the engineering and characterization of the antibody probes that are central to our single-molecule proteomics platform. The Scientist II will own antibody characterization, generating data that determines which candidates advance downstream. Beyond keeping pace with a rapidly growing probe pipeline, this scientist will apply mechanistic studies and binding characterization to improve upon probe discovery cycles.
They will integrate sequence, structural, and biophysical data to identify the properties that predict on-platform performance, and turn those insights into rational design rules. Working at the intersection of hands-on protein engineering and in silico design, they will shape our affinity maturation strategy and drive the success of the Nautilus platform.
- Apply protein engineering, antibody discovery, and affinity maturation strategies to design antibody variants with improved binding affinity
- Perform antibody purification, screening and characterization (e.g., BLI, SPR, DSF, SEC/SEC-MALS, Simple Western, ELISA etc.) to validate antibody engineering outcomes
- Identify, evaluate and implement new technologies and methods for antibody characterization and performance
- Apply AI/ML and in silico tools (e.g., protein language models, generative design, structure prediction) to guide antibody affinity maturation: generate and prioritize variant designs, translate model outputs into testable variant panels, and feed experimental binding and stability data back into the models to drive iterative design-build-test-learn cycles in partnership with computational biology and bioinformatics teams
- Coordinate/perform screening of antibodies on the Nautilus platform, analyze and interpret the results, and integrate on-platform performance with off-platform characterization data
- Interpret and communicate data clearly, translating complex results into actionable recommendations that inform prioritization and go/no-go decisions
- Collaborate closely with bioinformatics, computational biology, discovery, and assay development teams to formulate hypotheses, experimental priorities, and success criteria, and to advance antibodies through the pipeline
- Contribute to experimental strategy and project planning, proactively identifying risks, proposing alternative approaches, and adapting plans as new data emerge
- Present findings in meetings and document methods, results, and conclusions in a clear, reproducible manner (e.g., electronic lab notebook, technical reports)
- Foster a collaborative, knowledge-sharing environment by providing scientific input to peers, sharing best practices.