AI Biologist - Biologics Development
LatchBio
Summary
Latch builds rigorous scientific benchmarks for AI agents across biology. We work with frontier labs and pharma to measure whether agents can handle real biological workflows: the messy, multi-layer reasoning that matters in the field. We’re seeking an AI Biologist to join our TxBench team, pushing what AI can achieve in therapeutic antibody development.
Job description
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:
Build the task
- Turn real development workflows into file-based tasks with structured outputs and deterministic grading: developability triage, analytical/CMC characterization, PK and FcRn reasoning, immunogenicity, effector function and safety, and first-in-human dose projection
- Write gold-standard answers from raw data, with tolerances that reflect real replicate, integration, and fitting variability
- Curate data fixtures: developability panels, glycan LC-MS, charge-variant CE, intact/subunit MS, SEC-MALS, FcRn/FcγR sensorgrams, concentration-time PK, and immunogenicity and effector assays
- Partner with software engineers to turn your judgment into new workflows
Set the hard calls
- Test whether agents can tell a real liability from an artifact, such as a co-eluting glycan peak, a misassigned charge variant, or a single assay flag not confirmed by orthogonal readouts
- Test whether agents match effector function and affinity to the mechanism, rather than assuming “more is better”
- Test whether agents distinguish linear from target-mediated clearance, and judge a molecule against its target product profile
Judge the agent
- Interrogate agent reasoning step by step, and challenge conclusions that sound right but wouldn’t survive a development review or go/no-go decision
- Pinpoint where agents go wrong: why a step fails, what an expert would do instead, and what evidence would change the call
- Score agent confidence, rewarding explicit uncertainty over overconfident calls
Requirements
- 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
- Catered lunch and dinner daily
- Company outings
- Team off-sites
- Run club
- Cycling
- Boxing
- Ice skating
- Soccer
Company overview
LatchBio is a San Francisco-based cloud platform that provides comprehensive data infrastructure and analysis tools for biotechnology research. Founded in 2021, the company offers a unified data platform designed to connect bench scientists, computational scientists, IT teams, and R&D leadership into a cohesive framework. Their platform enables scientists to store, transform, and visualize bioinformatics data, making data analysis faster, more accessible, and cost-effective. LatchBio's solution includes workflow orchestration, scalable cloud computing resources, data storage, and interactive visualization tools specifically tailored for biological data analysis. The company serves hundreds of biotech companies and research organizations, helping them accelerate R&D milestones by eliminating infrastructure complexities. With backing from prominent investors including Lux Capital and Coatue, LatchBio has raised significant funding to develop its platform that supports various omics technologies including NGS, spatial transcriptomics, and single-cell analysis.