You will build, fine-tune, and deploy large biomolecular models such as OpenFold and ESM for antibody modeling and drug discovery workflows. Additionally, you will transform research prototypes into reliable, scalable components within federated training and evaluation pipelines.
You will scope and launch new federated data networks while managing end-to-end execution with pharmaceutical partners. Your role involves ensuring the Apheris Foundry platform is successfully integrated into live drug discovery programs.
Define scientific workflows and modeling strategies for antibody-antigen co-folding, binder prediction, and developability. Act as the primary scientific liaison between pharma partners and the internal ML/engineering teams to ensure model outputs align with biological reality.
Develop and improve machine learning models for molecular and structural biology to support drug design workflows. Collaborate with internal teams and external partners to build robust benchmarking strategies and resolve data quality issues.
You will own the marketing strategy, narrative, and end-to-end campaigns to position the company's AI-driven drug discovery platform to scientific leaders. This involves translating complex scientific benchmarks into compelling arguments and managing a small team to execute across various channels.
You will lead the expansion into predictive toxicology and quantitative biology by setting the scientific strategy and integrating workflows into the federated platform. You will also act as a scientific lead for customers and partners to drive the adoption of models in real drug-discovery pipelines.
Build and implement ML applications for structural biology, focusing on fine-tuning foundational models like OpenFold and ESMFold. Design scalable pipelines for training, inference, and deployment while collaborating with customers to solve real-world drug discovery problems.
