Drive the technical approach for ML applications in structural biology, particularly around fine-tuning and extending foundational models like OpenFold and ESMFold., Design and implement model extensions for specific tasks such as protein complex and binding affinity prediction, including data distillation, benchmarking, and evaluation pipelines., Work with customers and potentially academic partners to define data preprocessing, selection, and benchmarking strategies for novel training tasks involving protein structures, complexes, and multimodal biological data., Collaborate directly with customers and partners to identify their data integration needs and develop tailored strategies for leveraging their data within a secure, federated network., Build and maintain scalable, production-ready ML systems including training, inference, and deployment pipelines., Collaborate cross-functionally to ensure models address real-world drug discovery needs., Mentor and guide team members on a content level, supporting the planning and breakdown of complex structural biology modeling projects., Influence strategic decisions on model architecture, data infrastructure, and model deployment., Contribute to publications or open-source contributions where relevant.
