Develop and evaluate AI-based biomarkers using multimodal data.
Design, implement, and improve machine-learning models to predict patient outcomes and treatment response.
Contribute to the end-to-end model development lifecycle, including data preparation, training, evaluation, and validation.
Support the productionalization, launch, and monitoring of machine-learning models in collaboration with platform and product teams.
Conduct research and experimentation to improve model performance, robustness, generalizability, and interpretability.
Collaborate with biostatistics, clinical, and product partners to translate clinical questions into machine-learning solutions.
Contribute to scientific publications and conference submissions alongside the broader research team.

2+ years of industry experience using PyTorch or TensorFlow.
Experience contributing to machine-learning systems deployed or maintained in production environments.
Ability to clearly communicate complex technical concepts to cross-functional, non-ML collaborators.
Desired: Experience working with large-scale image data or computer vision models.
Familiarity with self-supervised representation learning (e.g., MoCo, DINOv2) and/or vision–language models (VLMs) and multimodal representation learning.
Interest in healthcare, medical imaging, or applied machine learning in regulated or high-impact domains.

Machine Learning Engineer – Model Dev

Key skills