Senior Machine Learning Engineer

About the role

Responsibilities

• Model development. Design, train, and evaluate clinical prediction models, with feature engineering across physiological time series and structured EHR context.
• Labels and ground truth. Define what you are actually predicting with clinical teams, build adjudication workflows, and understand the noise in your targets.
• Evaluation and testing infrastructure. Build the eval harnesses, backtesting, and regression suites that let us ship new model versions and new configurations with confidence, including how flagging behaves and whether explanations hold up.
• Clinical evaluation. Sensitivity, specificity, lead time, and alert burden as the care team experiences them. Threshold selection is a clinical decision as much as a statistical one.
• Robustness. Find where performance varies across facilities, settings, and demographics, and quantify it.
• Production and evidence. Ship with ML Ops support on serving and deployment, monitor real-world performance, and contribute to validation studies and regulatory submissions.

Requirements

• 5+ years building ML models that reached production and were used for real decisions
• Strong Python and modern deep learning frameworks, plus fluency in classical ML
• Experience with time-series or sequential data
• Evaluation practice covering calibration, class imbalance, and temporal leakage
• Experience building evaluation, backtesting, or model regression infrastructure
• Strong SQL and experience with production data
• Experience presenting model behavior and limitations to non-technical stakeholders