Machine Learning Engineer (Model Dev)
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The role
Develop and evaluate AI-based biomarkers using multimodal data, including whole-slide images, clinical metrics, and molecular profiles, to predict molecular traits and patient outcomes.
Design, implement, and improve machine-learning models to forecast treatment response and other outcomes in cancer care.
Contribute to end-to-end model development, including data preparation, training, evaluation, validation, and production monitoring.
Collaborate with biostatistics, clinical, and product teams to translate clinical questions into robust ML solutions and ensure generalizability.
Support productionization, deployment, monitoring, and governance of models with platform and product teams; drive interpretability and reliability.
Conduct research and experimentation to improve performance, robustness, and explainability; 2+ years of industry experience with PyTorch or TensorFlow; strong cross-functional communication.
Design, implement, and improve machine-learning models to forecast treatment response and other outcomes in cancer care.
Contribute to end-to-end model development, including data preparation, training, evaluation, validation, and production monitoring.
Collaborate with biostatistics, clinical, and product teams to translate clinical questions into robust ML solutions and ensure generalizability.
Support productionization, deployment, monitoring, and governance of models with platform and product teams; drive interpretability and reliability.
Conduct research and experimentation to improve performance, robustness, and explainability; 2+ years of industry experience with PyTorch or TensorFlow; strong cross-functional communication.
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