Research Scientist, Life Sciences (Large Molecules, Process Development)

Anthropic•San Francisco, CA

19h•$300,000 - $320,000•Onsite

About The Position

Anthropic’s Life Sciences team is building a research group focused on biological discoveries that combine AI with hands-on biological research. The Research Scientist will bring deep expertise in antibody process development and manufacturing, with hands-on experience expressing, purifying, characterizing, and formulating antibodies—from small screening batches through material for IND-enabling studies. The role connects AI-generated hypotheses with experimental data. Working alongside AI researchers and engineers, the scientist will build and run workflows that turn AI-designed sequences into well-characterized proteins and test AI predictions about expression, stability, and manufacturability against experimental results. The position is fully on-site in the San Francisco Bay Area. The scientist will develop end-to-end protein production and characterization capabilities, establish the laboratory and its workflows, evaluate AI-designed antibodies, and plan the chemistry, manufacturing, and controls path toward IND-enabling studies. The role also involves coordinating technology transfer and manufacturing with external contract manufacturers, maintaining high-quality experimental documentation, and communicating findings across biology, AI research, and life sciences teams.

Requirements

PhD in biochemical engineering, chemical engineering, biochemistry, or a related field.
Demonstrated experience in process development and manufacturing of antibodies or other recombinant proteins, including material supplied for IND-enabling or clinical studies.
Recent, sustained hands-on laboratory work.
Comfort navigating ambiguity and defining problems in a rapidly evolving research environment.
Ability to work independently and collaborate effectively across cross-functional teams.
Strong written and verbal communication skills.

Nice To Haves

Five or more years of biologics process development experience beyond the PhD.
Depth in both upstream development, including cell line development and bioreactors, and downstream development, including chromatography and filtration, with working knowledge of analytical and formulation development.
Experience establishing a protein production or process development laboratory, including laboratory automation and high-throughput, plate-based expression and purification.
Experience directing technology transfer and GMP manufacturing at contract development and manufacturing organizations, with contributions to CMC sections of an IND.
Statistics or coding skills, such as design of experiments or Python for data analysis and automation.
Experience using AI or ML tools in the life sciences.
Publications and/or patents demonstrating scientific impact.

Responsibilities

Develop and run end-to-end expression and purification processes for antibodies and other recombinant proteins, including transient and stable expression in HEK293 and CHO mammalian cells, affinity capture, polishing chromatography, buffer exchange, and formulation.
Characterize proteins using appropriate analytical methods, such as SEC, CE-SDS, icIEF, LC-MS, DLS, thermal stability, endotoxin testing, and binding kinetics assays including SPR and BLI.
Build the laboratory for protein production and process development by selecting, installing, and qualifying equipment and establishing workflows that scale from plate-based production of hundreds of designs to gram quantities of a lead.
Evaluate AI-designed antibodies for manufacturability, including titer, aggregation, stress stability, viscosity, and chemical liabilities; generate experimental readouts and share results with research teams.
Plan the CMC path toward IND-enabling studies, covering cell line development, scale-up, analytical methods, and stability.
Coordinate technology transfer and manufacturing with external contract manufacturers.
Ensure experimental data across programs is high quality and well documented, including batch records, methods, raw data, and failure modes.
Communicate findings clearly to biology, AI research, and life sciences audiences.