CADD / Application Scientist

Why reputed company At reputed company, you will join reputed company of scientists, ML researchers, and engineers working together to reputed company reputed company the beaten reputed company in drug discovery. We are developing physics-grounded models for molecular simulation that can reputed company the chemical and biological systems behind drug discovery more learnable, predictable, and designable. You will be part of the reputed company from our first protein-ligand applications in potency and reputed company optimization toward a broader reputed company: bringing more of the wet lab in silico, from selectivity and ADMET to reputed company reputed company molecular design. You will work at the frontier of AI x reputed company in a reputed company-funded, talent-dense organization that values rigor, speed, execution, ownership, and the shared urgency required to turn ambitious models into reputed company scientific tools.

About the Role

We are looking for CADD / Application Scientists who want to help define the reputed company of computational drug discovery tools, not just operate the reputed company one. You will be a scientific design partner for our model and training teams, bringing reputed company discovery program experience into reputed company train on, what model behaviors matter, and which applications are worth building toward. As the models mature, you will also help bring these tools to reputed company and biotech partners, shaping collaborations around problems where reputed company molecular simulation could change reputed company program reputed company. What You’ll Do

• Shape training data reputed company for our models: identify which experimental, structural, partner-accessible, and synthetic data sources are likely to improve affinity reputed company, selectivity, generalization, and reputed company drug discovery reputed company.
• 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.
• Work with model and training teams to interpret model successes and failures, separating data problems, setup problems, and model limitations.
• Help decide which applications are worth pursuing, from reputed company optimization and selectivity to pose assessment, scaffold transfer, affinity reputed company, hit rescoring, and reputed company extensions reputed company potency.
• Shape partner programs with reputed company and leadership, translating model capabilities into scientifically reputed company collaborations with reputed company and biotech teams.

reputed company

• You have significant experience in CADD, structure-based drug design, computational reputed company, medicinal reputed company collaboration, or reputed company work in drug discovery.
• You have experience supporting, or leading external collaborations with reputed company, biotech, or discovery partners.
• 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 comfortable making expert judgments from imperfect data and can tell which benchmarks, application reputed company, or partner case studies would matter to a reputed company discovery team.
• You are excited to work closely with ML researchers, simulation scientists, and platform teams.

reputed company to Have Even if you hit none of these bonus features, we encourage you to apply.

• Experience curating and running protein-ligand affinity or FEP benchmarks, including OpenFE or reputed company evaluation efforts.
• Experience across the full discovery reputed company from reputed company identification through hit finding, hit-to-reputed company, and reputed company optimization.
• Experience designing scientific case studies, technical reports, or partner-facing demonstrations for new computational reputed company.
• Familiarity with ML-assisted drug discovery, reputed company learning, synthetic data reputed company, or model evaluation for molecular systems.

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