AI Biologist - Peptide Discovery
LatchBio
Summary
You will be the scientific authority on ground truth, testing whether agents can reason like an expert peptide discovery scientist. AI agents today struggle to interpret raw selection, biophysical, and structural data, to separate true hits from artifacts, and to make the calls that move a peptide from target to candidate. The data often misleads: enrichment can be a propagation artifact, protection is not contact, and binding is not function. Knowing what the evidence actually supports is the work.
Job description
About the Role
You will be the scientific authority on ground truth, testing whether agents can reason like an expert peptide discovery scientist. AI agents today struggle to interpret raw selection, biophysical, and structural data, to separate true hits from artifacts, and to make the calls that move a peptide from target to candidate. The data often misleads: enrichment can be a propagation artifact, protection is not contact, and binding is not function. Knowing what the evidence actually supports is the work.
You will work with software engineers and scientists to build the tasks and datasets behind our benchmarks. They span linear, macrocyclic, and constrained peptides against protein targets, including protein–protein interactions, from target tractability to candidate nomination.
Build the task
- Define what expert reasoning looks like for a peptide discovery question, and write gold-standard answers with the rationale an experienced peptide scientist would defend
- Design realistic scenarios across the discovery arc: target tractability, hit generation, hit confirmation and kinetics, binding-site mapping, functional confirmation, and lead optimization
- Curate datasets from real programs: display-selection read counts, SPR/BLI sensorgrams, fluorescence-polarization titrations, affinity-selection MS, HDX-MS, crosslinking-MS, NMR chemical-shift perturbation, dose-response, and permeability and stability data
- Build analysis pipelines in Python, from enrichment analysis to kinetic and dose-response fitting, that produce verifiable ground truth with tolerances that hold across replicates and fitting routines
- Partner with software engineers to turn your judgment into new workflows
Set the hard calls
- Test whether agents distinguish true signal from artifact, such as propagation or expression artifacts enriched across rounds, sensorgrams distorted by aggregation or superstoichiometric binding, or HDX protection mistaken for direct contact
- Adjudicate conflicting evidence: reconcile orthogonal binding-site datasets, decide between competitive and allosteric engagement, and identify which experiment would settle the call
- Test whether agents weigh the right tradeoffs: binding versus function, potency versus developability, and when added constraint or affinity costs a property that matters more
Judge the agent
- Interrogate agent reasoning step by step, and challenge conclusions that sound right but would not survive a project review or lab meeting
- 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 on ambiguous data
Requirements
- PhD in peptide chemistry, biochemistry, biophysics, structural biology, medicinal chemistry, or a related field
- Senior, hands-on peptide discovery experience: personally analyzed real program data, not just supervised it
- Direct experience with at least one of: display selection (phage, mRNA, ribosome, RaPID, or DNA-encoded libraries), SPR/BLI or FP binding kinetics, binding-site mapping by structural MS or NMR, or peptide developability (PAMPA/Caco-2 permeability, proteolytic and serum stability)
- Deep expertise in at least one area (display and selection, biophysics, structural mapping, or peptide medicinal chemistry) and working fluency across the others
- Strong command of experimental design, quantitative data analysis in Python or R, and statistics
- 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: peptide format and chemistry experience, such as 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, or computational design with Rosetta or RFdiffusion
Benefits
- Catered lunch and dinner daily
- Monthly parking
- Company outings
- Team off-sites
- Waterfront office in Mission Bay
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.