Machine Learning Engineer

BD is hiring a Staff Machine Learning Engineer to help develop clinically meaningful AI/ML solutions for continuous physiological monitoring, disease detection, and predictive clinical decision support. The role focuses on building end-to-end algorithms and deploying them across cloud systems and edge medical devices, working with multi-modal healthcare data to support monitoring and risk workflows.

• Lead the design, development, validation, and deployment of AI/ML algorithms for continuous physiological monitoring, novel vital sign and physiological parameter development, disease detection, risk stratification, and early warning systems.
• Develop models using multi-modal sources, including physiological waveforms and time-series signals such as PPG, blood pressure, capnography, and other monitoring signals, along with bedside and wearable sensor data, electronic health records, and structured clinical data.
• Translate clinical and physiological understanding into robust algorithm design, including feature selection, model architecture, performance targets, and clinically interpretable outputs.
• Build end-to-end development pipelines for data ingestion, signal quality assessment, annotation, feature extraction, model training, validation, robustness testing, and post-market performance monitoring where applicable.
• Partner with clinical, medical affairs, regulatory, quality, and product teams to align development with clinical context, usability, safety, and regulatory considerations.
• Develop and optimize algorithms for cloud environments and for edge or embedded medical devices, accounting for latency, memory, compute, power, cybersecurity, and reliability constraints.
• Support verification, validation, documentation, risk analysis, and design controls for regulated medical technology products.
• Contribute to intellectual property generation, technical strategy, research roadmaps, and external scientific engagement.

• 7+ years of industry experience in AI/ML, algorithm development, biomedical signal processing, healthcare analytics, medical devices, or related technical areas.
• Master’s or PhD degree preferred in Electrical Engineering, Biomedical Engineering, Computer Science, Data Science, Applied Mathematics, Statistics, Computational Biology, or a related engineering or quantitative field.
• Deep expertise in machine learning and AI model development, including classical and modern approaches.
• Hands-on experience with physiological time-series data, biomedical signals, medical device data, or clinical datasets.
• Understanding of human physiology as it relates to patient monitoring.
• Demonstrated experience developing algorithms from early feasibility through productization or deployment.
• Experience with multi-modal healthcare data, including sensor signals, EHR data, imaging data, lab data, and clinical outcomes.
• Strong programming skills in Python and experience with ML/AI frameworks such as PyTorch, Tensor Flow, scikit-learn, or equivalent tools.
• Experience building and deploying algorithms in cloud environments using Azure, AWS, or GCP.
• Experience optimizing models for constrained environments, including embedded systems, edge devices, or near-real-time medical technology platforms.
• Strong understanding of model evaluation, including sensitivity, specificity, AUROC, calibration, false alarm burden, clinical utility, robustness, and generalizability.
• Excellent communication skills, with the ability to explain complex technical concepts to engineering, clinical, regulatory, and business stakeholders.

• Python;
  PyTorch;
  Tensor Flow; scikit-learn
• Azure; AWS; GCP
• Transformer-based models and foundation-model approaches

• Experience developing AI/ML…