Anthropic Research Scientist at Anthropic specializing in therapeutic antibody design and engineering. Lead antibody programs end to end, integrating AI-generated hypotheses with experimental data.
Responsibilities
Anthropic's Life Sciences team is building a research group focused on biological discoveries by combining AI with hands-on biological research. We're seeking a research scientist with deep expertise in therapeutic antibodies to join the team. Our goal is to speed up progress in the life sciences by an order of magnitude, and this role puts that mission into practice. You'll be an expert in antibody design and engineering who has taken antibodies through the whole process, from target selection and lead optimization to candidate selection and IND-enabling studies. Above all, you'll close the loop between AI-generated hypotheses and experimental data to share with the community. Working alongside AI researchers and engineers, you'll drive antibody programs end to end and pressure-test AI-designed antibodies against the bar a real drug candidate has to clear.
This role is fully on-site in the San Francisco Bay Area.
Design and engineer therapeutic antibodies and oversee programs end to end, including target and epitope selection, choice of format (IgG, Fab, scFv, VHH, bispecific), humanization, affinity maturation, Fc engineering, and candidate selection
Define the screening cascade for each program, from binding kinetics (SPR, BLI), epitope binning, and species cross-reactivity to cell-based functional assays and developability (expression, aggregation, polyreactivity, stability, viscosity, immunogenicity risk)
Plan the path from lead to IND-enabling studies, including in vivo pharmacology, pharmacokinetics, toxicology, and the handoff to process development and manufacturing
Coordinate antibody production, characterization, and in vitro and in vivo studies with external contract research organizations and academic partners
Evaluate AI-designed antibodies, drive them to experimental readout, and feed results back to research teams
Ensure experimental data across programs is high quality and well documented, including sequences, protocols, raw data, and failure modes
Communicate findings clearly to biology, AI research, and life sciences audiences
Qualification
PhD in biochemistryStructural biology
Required
PhD in biochemistry, protein engineering, immunology, molecular biology, or a related field
Demonstrated experience in therapeutic antibody discovery and engineering, including at least one antibody you helped take from discovery through candidate selection and IND-enabling studies
Working knowledge of what an antibody IND requires across pharmacology, pharmacokinetics, toxicology, and chemistry, manufacturing, and controls (CMC)
Comfort navigating ambiguity and defining problems in a rapidly evolving research environment
Ability to work independently while collaborating effectively across cross-functional teams, with strong written and verbal communication skills
Preferred
5+ years of therapeutic antibody experience beyond the PhD
Track record of advancing more than one antibody program into IND-enabling studies or the clinic, with contributions to pre-IND or IND documents
Hands-on experience with antibody expression, purification, and biophysical characterization
Experience directing antibody production, assays, and in vivo studies at contract research organizations
Structural biology, computational protein design, or coding skills, for example Python for sequence and assay data analysis or structure prediction and protein design tools
Experience using AI or ML tools in the life sciences
