Description:
The Role We are seeking a Senior Machine Learning Engineer to transition our core signal analysis pipeline from validated prototype to production-ready solution. You’ll help us build scalable infrastructure, optimize model performance, and contribute directly to clinical and regulatory milestones. You’ll work closely with our CTO and clinical collaborators, contributing to ongoing validation efforts and playing a pivotal role in shaping our ML strategy.
Key Responsibilities
- Develop and deploy advanced machine learning architectures (classification and regression) spanning classical algorithms (ensemble methods, DT, RF, SVMs, etc.), deep learning networks (CNNs, RNNs), and long short-term memory (LSTM) models for temporal cardiovascular signal analysis
- Enhance model performance through advanced signal analysis, feature engineering, ensemble optimization, and multi-site validation
- Develop signal quality assessment and preprocessing pipelines for timeseries signals (e.g. ECG,…)
- Integrate diverse data modalities including medical notes (disease history, medications, etc.), physics-based digital twin outputs (pressure & flow), and ultrasound measurements from various arteries
- Expand predictive capabilities to additional heart failure metrics including pulmonary artery pressure, pulmonary vascular resistance, and related cardiovascular parameters
- Build calibration and explainability frameworks meeting clinical standards and emerging Good ML Practice guidelines
- Establish automated ML workflows including data versioning, training pipelines, and validation frameworks
- Translate technical developments into regulatory documentation and clinical study protocols
Required Qualifications
- PhD in Biomedical Engineering, Computer Science, or related field; or MS with 4+ years healthcare ML experience
- Proven experience developing ML models for physiological signals, preferably cardiovascular applications using custom datasets as well as publicly available datasets (e.g. MIMIC, Mayo, etc.)
- Strong proficiency in Python ML stack (scikit-learn, PyTorch/TensorFlow, pandas) + signal processing
- Track record of translating research algorithms to clinical deployment or production systems
- Experience with multimodal data integration and feature extraction from clinical datasets
- Knowledge of FDA Software as Medical Device (SaMD) frameworks or medical device regulations