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About the Company
We are a digital health startup developing non-invasive, real-time hemodynamic monitoring technology for cardiovascular care. Our platform combines wearable biosensors with advanced machine learning to deliver clinically actionable insights for heart failure and vascular risk management. We're now preparing to scale our algorithms from validated prototypes to production-grade, regulatory-compliant systems.
About the Role
We’re hiring a Senior Machine Learning Engineer to lead the transition of our cardiovascular signal analysis models from research to real-world deployment. You’ll work closely with our CTO and clinical team to optimize model accuracy, enhance data pipelines, expand validation datasets, and prepare for regulatory submissions. You’ll also play a key role in building our MLOps infrastructure.
Key Responsibilities
Build and deploy ML models (classification/regression) using classical methods (DT, RF, SVMs) and deep learning (CNNs, RNNs, LSTM)
Develop preprocessing pipelines for ECG, PPG, BP, and imaging data
Implement signal quality metrics, ensemble optimization, and multi-site model validation
Integrate multimodal clinical data: medical notes, digital twin outputs, ultrasound measurements
Expand model predictions to new heart failure markers (e.g. pulmonary artery pressure, PVR)
Build interpretable models aligned with clinical explainability standards
Establish robust ML workflows: versioned data, automated training, reproducible validation
Translate technical work into documentation for regulatory approval and clinical studies
First 90 Days
Deliver a working ML model on a defined patient dataset
Build and document a high-quality data pipeline (ETL, labeling, validation)
Establish baseline model metrics (accuracy, recall, F1, AUC)
Draft clinical-grade documentation of data lineage, model assumptions, and architecture
Begin compliance review aligned with HIPAA and Good ML Practices
Tools & Stack
Languages: Python (scikit-learn, PyTorch or TensorFlow, pandas, NumPy)
Signal Processing: Custom and open-source cardiovascular signal tools
Data: Custom clinical datasets, public sources (MIMIC, Mayo), multimodal records
MLOps: Model versioning, reproducibility, documentation
Compliance: HIPAA, SaMD, GMLP standards
Requirements
PhD in Biomedical Engineering, CS, or related; OR MS + 4+ years healthcare ML experience
Hands-on experience developing ML models for physiological signals (ideally cardiovascular)
Experience deploying models from research to production
Strong background in signal processing and multimodal feature engineering
Knowledge of FDA medical device regulations and SaMD compliance
Fluent English and ability to work overlapping US hours
Preferred Qualifications
Experience with edge deployment for wearables
Clinical text processing (EHR, medical notes)
Background in cardiovascular physiology and biomarkers
Experience with ultrasound data and vascular measurements
Familiarity with FHIR/HL7, HIPAA-compliant pipelines
Additional Details
Work arrangement: Remote, offshore with US time zone overlap
Interview process: Technical interview + management interview
Soft skills: Documentation, scientific communication, cross-functional collaboration
Compensation: TBD
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