Technology jobs in San Francisco, CA

About the role

You will be the scientific authority on ground truth, testing whether agents can reason like an expert biologics development scientist. The benchmark covers monoclonal antibodies, Fc-fusions, and bispecifics in the window after lead nomination and before IND. AI agents today struggle to interrogate raw analytical traces and preclinical data, separate real quality-attribute failures from analytical artifacts, and make advance, engineer, or kill calls an expert would stand behind. You will work with software engineers and scientists to build the tasks and datasets behind our benchmarks. Your judgment will set the standard agent outputs are held to, including:

What you'll bring

• PhD in protein engineering, biochemistry, analytical chemistry, pharmacology, or a related field
• Hands-on analysis of antibody development data.
• You’ve done the analysis yourself, not just supervised it
• Deep expertise in at least one area (analytical/CMC characterization, large-molecule DMPK, protein engineering and developability, or non-clinical safety) and working fluency across the others
• Command of how readouts map to critical quality attributes, PK and effector properties, and release or comparability decisions
• Python or R for data analysis, including parsing chromatograms and electropherograms, kinetic fitting, or PK modeling
• AI-ready from day one: daily use of Claude, Cursor, or similar agents, with the judgment to spot where model reasoning is technically correct but scientifically unsound
• Confidence to overrule an AI agent when the evidence demands it, and the openness to update when shown wrong
• Ability to calibrate confidence and communicate uncertainty rather than defaulting to binary calls
• Bonus: antibody format and Fc engineering experience (IgG subclasses, YTE/LS, effector-silencing, bispecific architectures)
• Bonus: familiarity with MAM, ICH Q6B/Q5E, FDA immunogenicity guidance, IMGT, SAbDab, and IEDB

Benefits

Locations