AI Biologist - Peptide Discovery
L
LatchPeptide discovery
Remote (anywhere), hybrid, or onsite in SFContractSenior
Salary120,000 - 180,000 USD per year
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Job Details
- Required Skills
- PythonR
Requirements
- PhD in peptide chemistry, biochemistry, biophysics, structural biology, medicinal chemistry, or a related field.
- Senior, hands-on peptide discovery experience, including personally analyzing real program data.
- Direct experience with at least one of: display selection, SPR/BLI or FP binding kinetics, binding-site mapping by structural MS or NMR, or peptide developability.
- Deep expertise in at least one area: display and selection, biophysics, structural mapping, or peptide medicinal chemistry.
- Working fluency across the other peptide discovery areas.
- Strong command of experimental design, quantitative data analysis in Python or R, and statistics.
- Daily use of Claude, Cursor, or similar AI agents, with the ability to identify scientifically unsound model reasoning.
- Ability to calibrate confidence and communicate uncertainty rather than defaulting to binary calls.
- Bonus: experience with macrocyclization, hydrocarbon stapling, N-methylation, D- and non-canonical residues, or HELM encoding.
- Bonus: target tractability or de novo design experience, such as PPI hotspot analysis, human-genetic evidence for therapeutic direction, Rosetta, or RFdiffusion.
Responsibilities
- Define expert reasoning for peptide discovery questions and write gold-standard answers with supporting rationale.
- Design realistic benchmark scenarios across target tractability, hit generation and confirmation, kinetics, binding-site mapping, functional confirmation, and lead optimization.
- Curate real-program datasets, including selection, binding, structural-mapping, dose-response, permeability, and stability data.
- Build Python analysis pipelines for enrichment analysis and kinetic and dose-response fitting, with verifiable ground truth across replicates and fitting routines.
- Partner with software engineers to translate scientific judgment into benchmark workflows.
- Test whether agents distinguish true signals from experimental artifacts and interpret conflicting evidence.
- Evaluate tradeoffs among binding, function, potency, and developability.
- Interrogate agent reasoning, identify errors, and describe what evidence would change a conclusion.
- Score agent confidence and reward appropriate uncertainty on ambiguous data.
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