AI/ML Engineer, Oncology AI
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The role
Design and implement novel biophysical modeling and foundation-model driven analysis of multi-modal clinical and genomic data to discover biomarkers and inform target selection in oncology.
Develop analytical solutions using Python, PyTorch, scikit-learn, and related tools; translate research into practical tools that support patient selection and asset development.
Collaborate with biology, genomics, and medicine experts to identify opportunities to apply the latest ML advances and to validate predictive models.
Create automated, agentic processes for predictive model validation, deployment, and operationalization at scale in production environments.
Deploy your algorithms to production to extract actionable insights from large-scale datasets and enable next-generation therapies.
Maintain strong software engineering practices, communicate learnings across cross-functional teams, and stay current with advances in AI/ML and cancer biology.
Develop analytical solutions using Python, PyTorch, scikit-learn, and related tools; translate research into practical tools that support patient selection and asset development.
Collaborate with biology, genomics, and medicine experts to identify opportunities to apply the latest ML advances and to validate predictive models.
Create automated, agentic processes for predictive model validation, deployment, and operationalization at scale in production environments.
Deploy your algorithms to production to extract actionable insights from large-scale datasets and enable next-generation therapies.
Maintain strong software engineering practices, communicate learnings across cross-functional teams, and stay current with advances in AI/ML and cancer biology.
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