ML / AI Engineer

Role overview Build and operate machine learning systems that support clinical triage, risk prediction, diagnostic assistance, and decision making. The work spans model development, evaluation, deployment, and ongoing oversight, with a focus on making system behavior understandable and ensuring uncertain cases can receive human review.

Responsibilities - Develop, assess, and deploy models for clinical triage, risk forecasting, and decision support. - Design workflows that bring clinicians into the loop when a model is uncertain. - Maintain production ML operations, including performance monitoring, drift detection, model versioning, and reproducible workflows. - Explain model behavior, limitations, and evaluation results for clinical and compliance review. - Investigate model errors and assess how performance varies across groups and use cases.

Requirements - At least three years delivering machine learning systems into production. - Practical experience with PyTorch or TensorFlow. - Strong evaluation skills, including bias assessment, calibration, and failure analysis. - Experience working with de-identified or otherwise regulated data.

Nice to have - Experience with clinical language processing, medical imaging, or LLM-based agents.