Research Scientist, Life Sciences (Large Molecules, Antibody Design)

Anthropic•San Francisco, CA

7d•Onsite

About The Position

Anthropic’s Life Sciences team is building a research group focused on biological discoveries by combining AI with hands-on biological research. The Research Scientist will bring deep expertise in therapeutic antibody design and engineering, with experience spanning target selection and lead optimization through candidate selection and IND-enabling studies. The role aims to accelerate progress in life sciences by closing the loop between AI-generated hypotheses and experimental data and sharing findings with the community. Working alongside AI researchers and engineers, the scientist will drive antibody programs end to end and evaluate AI-designed antibodies against the standards required of real drug candidates. The role is fully on-site in the San Francisco Bay Area.

Requirements

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 helped from discovery through candidate selection and IND-enabling studies.
Working knowledge of antibody IND requirements 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 and collaborate effectively across cross-functional teams, with strong written and verbal communication skills.

Nice To Haves

Five or more 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, such as Python for sequence and assay data analysis or use of structure prediction and protein design tools.
Experience using AI or machine-learning tools in the life sciences.
Publications and/or patents demonstrating scientific impact.

Responsibilities

Design and engineer therapeutic antibodies and oversee programs end to end, including target and epitope selection, format choice (IgG, Fab, scFv, VHH, or bispecific), humanization, affinity maturation, Fc engineering, and candidate selection.
Define screening cascades for each program, covering binding kinetics (SPR, BLI), epitope binning, species cross-reactivity, cell-based functional assays, and developability measures such as expression, aggregation, polyreactivity, stability, viscosity, and immunogenicity risk.
Plan the path from lead to IND-enabling studies, including in vivo pharmacology, pharmacokinetics, toxicology, and 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.