Achira
CADD / Application Scientist
New York Office
Sponsorship not specifiedDetected 56 days ago
Machine LearningLab ResearchLeadershipCollaboration
About the role
- We are looking for CADD / Application Scientists who want to help define the next generation of computational drug discovery tools, not just operate the current one.
Responsibilities
- Shape training data strategy for our models: identify which experimental, structural, partner-accessible, and synthetic data sources are likely to improve affinity prediction, selectivity, generalization, and downstream drug discovery utility.
- Own data and structure curation for high-value protein-ligand systems end-to-end: connect affinity measurements to assay context, prepare structures, assign protein and ligand states, review or generate poses, and label assumptions and uncertainty.
- Help decide which applications are worth pursuing, from lead optimization and selectivity to pose assessment, scaffold transfer, affinity prediction, hit rescoring, and future extensions beyond potency.
- Shape partner programs with BD and leadership, translating model capabilities into scientifically credible collaborations with pharma and biotech teams.
- You will be a scientific design partner for our model and training teams, bringing real discovery program experience into what we train on, what model behaviors matter, and which applications are worth building toward.
- We are developing physics-grounded models for molecular simulation that can make the chemical and biological systems behind drug discovery more learnable, predictable, and designable.
Requirements
- You have strong intuition for protein-ligand binding, ligand poses, assay artifacts, protonation / tautomer states, waters, cofactors, ligand strain, and where modeling workflows quietly go wrong.
- You are excited to work closely with ML researchers, simulation scientists, and platform teams.
Nice to have
- Experience curating and running protein-ligand affinity or FEP benchmarks, including OpenFE or related evaluation efforts.
This listing is sourced directly from Achira's careers page and normalized into a canonical job model.