Harmonize your Wet and Dry Lab. Backed by Lux.
About the role
You will contribute to our technical approach for evaluating agent performance at reasoning across the drug discovery and development pipeline, from target validation through IND-enabling studies, and translating preclinical data into the decisions that advance (or kill) a program. You will work as part of a team of software engineers and scientists building datasets and AI agent tasks across the end-to-end drug development pipeline, including: Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Advanced degree (MS/PhD/PharmD) in pharmacology, toxicology, pharmaceutical sciences, medicinal chemistry, or a related biomedical field, or equivalent industry experience
- 3+ years of hands-on experience in pharma or biotech drug discovery/development
- Direct experience with lead optimization, candidate nomination, or IND-enabling study design in at least one modality
- Experience interpreting PK/PD, ADME, and GLP toxicology data to inform dose selection and risk assessment
- Familiarity with IND-enabling regulatory requirements and the nonclinical data package expected by FDA/EMA is s plus
- Working knowledge of core pharmacology/tox study types: GLP tox, safety pharmacology, genotoxicity, biodistribution, bioanalytical assays
More about this role
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 - Drug Development to join our team working at the frontier of what artificial intelligence can achieve in drug discovery and development.
You will contribute to our technical approach for evaluating agent performance at reasoning across the drug discovery and development pipeline, from target validation through IND-enabling studies, and translating preclinical data into the decisions that advance (or kill) a program.
You will work as part of a team of software engineers and scientists building datasets and AI agent tasks across the end-to-end drug development pipeline, including:
Designing realistic program case studies spanning target validation, lead optimization, candidate nomination, and IND-enabling studies across modalities
Curating multi-modal preclinical data bundles: SAR/potency panels, ADME/PK profiles, in vivo efficacy, GLP toxicology, safety pharmacology, and biodistribution...
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