Scientist II, Antibody Engineering and Characterization

Nautilus Biotechnology•San Carlos, CA

8d•$126,000 - $182,000

About The Position

Nautilus is hiring a Scientist II to engineer and characterize antibody probes for its single-molecule proteomics platform. The role owns antibody characterization and generates data to determine which candidates advance downstream. The scientist will use mechanistic studies and binding characterization to improve probe discovery cycles, integrate sequence, structural, and biophysical data to identify properties that predict on-platform performance, and develop rational design rules. The position combines hands-on protein engineering with in silico design to shape affinity maturation strategy and support the Nautilus platform. The scientist will design antibody variants, purify and characterize antibodies, evaluate new characterization technologies, and use AI/ML and in silico tools to guide iterative design-build-test-learn work in partnership with computational biology and bioinformatics teams. They will coordinate or conduct antibody screening on the Nautilus platform, connect on-platform outcomes with off-platform characterization, communicate evidence-based recommendations, and collaborate across discovery, assay development, bioinformatics, and computational biology. The role also contributes to project planning, documents methods and findings reproducibly, and supports a collaborative, knowledge-sharing team culture.

Requirements

Direct experience characterizing antibodies and protein-protein interactions using BLI, SPR, Western blot, ELISA, and/or other biophysical methods.
Hands-on experience developing, troubleshooting, and fine-tuning assays across technologies such as SPR, BLI, ELISA, and Western blot.
Protein engineering experience using structural or computational insights to design and test antibody variants, such as through rational mutagenesis or affinity engineering.
Experience with structure-prediction and modeling tools.
PhD in protein engineering, structural biology, biochemistry, or a related field.
Two to three years of relevant industry experience.

Nice To Haves

Experience measuring antibody binding to short linear peptides or epitopes.
Experience applying AI/ML or computational methods, such as protein language models, generative design, or structure-based deep learning, to guide antibody affinity maturation and translate model outputs into testable variant designs.
Familiarity with Python and ML frameworks or computational structural biology pipelines.
Familiarity with antibody discovery platforms such as phage display, yeast display, or ribosome display.
Experience with proteomics platforms.

Responsibilities

Apply protein engineering, antibody discovery, and affinity maturation strategies to design antibody variants with improved binding affinity.
Purify, screen, and characterize antibodies using methods such as BLI, SPR, DSF, SEC/SEC-MALS, Simple Western, and ELISA to validate engineering outcomes.
Identify, evaluate, and implement technologies and methods for antibody characterization and performance.
Use AI/ML and in silico tools to guide antibody affinity maturation, prioritize variant designs, translate model outputs into testable panels, and feed experimental binding and stability data back into models through iterative design-build-test-learn cycles.
Coordinate or perform antibody screening on the Nautilus platform, analyze and interpret results, and integrate on-platform performance with off-platform characterization data.
Interpret and communicate data clearly, translating complex results into actionable recommendations for prioritization and go/no-go decisions.
Collaborate with bioinformatics, computational biology, discovery, and assay development teams to develop hypotheses, experimental priorities, and success criteria, and advance antibodies through the pipeline.
Contribute to experimental strategy and project planning by identifying risks, proposing alternatives, and adapting plans as data emerge.
Present findings and document methods, results, and conclusions clearly and reproducibly, including in electronic lab notebooks and technical reports.
Provide scientific input to peers and share best practices to foster collaboration and knowledge sharing.