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 our customers and academic partners to define data preprocessing, selection, and benchmarking strategies for novel training tasks involving protein structures, complexes, and multimodal biological data., Design, build, and maintain scalable machine learning models and the pipelines needed for training, inference, and deployment in production., Collaborate cross-functionally to ensure models address real-world drug discovery needs., Mentor and guide peers on a content level, supporting the planning and breakdown of complex structural biology modeling projects., Make strategic decisions on model architecture, data infrastructure, and model deployment., Contribute to publications or open-source contributions where relevant.
