Senior Machine Learning Engineer, Predictive Modeling & Applied AI

about 3 hours ago

H1B Sponsor

Responsibilities

Build, train, and validate deep learning models for oncology real-world data.
Develop predictive models for digital twins, endpoint prediction, trial optimization, and treatment effect estimation.
Apply transfer learning and domain adaptation across data sources and oncology indications.
Support client engagements by building predictive models for specific use cases.
Collaborate with product, engineering, and data teams to create scalable solutions.
Document model design and explain its behavior to technical and non-technical partners.
Stay current with deep learning methods and integrate promising approaches into work.

Advanced degree in a quantitative or technical field or equivalent industry experience.
At least 5 years of experience building and shipping deep learning models.
Fluency in modern deep learning methods, including transformer architectures and neural networks.
Proficiency in Python and a deep learning framework like PyTorch or TensorFlow.
Experience with large-scale, longitudinal datasets, ideally in healthcare.
Ability to take models from research to production with a focus on reproducibility and maintainability.
Comfortable in a fast-paced, matrixed environment and able to balance multiple initiatives.
Ability to translate technical concepts into clear explanations for diverse stakeholders.