• Design, implement, and evaluate scalable modular model architectures that allow specialization and efficient use of computation • Develop and test methods that learn richer contextual and temporal representations by predicting or aligning different views, frames, or modalities • Improve representation-learning pipelines by experimenting with data preparation strategies, augmentation approaches, training schedules, and hyperparameter settings to increase robustness across modalities • Build reproducible training and evaluation workflows and run experiments at scale; maintain clear experiment logs and analyses • Measure model effectiveness on clinically relevant downstream tasks (e.g., classification, detection, segmentation, retrieval, temporal reasoning) and produce comparison reports and ablation studies • Collaborate with data engineers, clinicians, and researchers to curate and prepare datasets while following privacy and governance requirements • Produce well-documented code, experiment artifacts, internal reports, and, where appropriate, contribute to technical write-ups or presentations