Ai Biologist - Peptide Discovery

Latch builds rigorous scientific benchmarks (benchmarks.bio) 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 Peptide Discovery team, pushing what AI can achieve in therapeutic peptide discovery.

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. Your judgment will set the standard agent outputs are held to, including:

• 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 sensor grams, 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

• Test whether agents distinguish true signal from artifact, such as propagation or expression artifacts enriched across rounds, sensor grams 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 develop ability, and when added constraint or affinity costs a property that matters more

• 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

• PhD in peptide chemistry, biochemistry, biophysics, structural biology, medicinal chemistry, or a related field

• Senior, hands-on peptide discovery experience: you have 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 develop ability (PAMPA/Caco-2 permeability, proteolytic and serum stability)

• Deep expertise in at least one area (display and selection, biophysics,…