Staff Machine Learning Engineer





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BD is one of the largest global medical technology companies in the world. Advancing the world of health™ is our Purpose, and it’s no small feat. It takes the imagination and passion of all of us—from design and engineering to the manufacturing and marketing of our billions of MedTech products per year—to look at the impossible and find transformative solutions that turn dreams into possibilities.





Job Description

About the Role

We are seeking a highly experienced Staff Machine Learning Engineer to help advance next-generation medical technologies for continuous physiological monitoring, disease detection, and predictive clinical decision support.

In this role, you will develop clinically meaningful algorithms using multi-modal healthcare data, including physiological sensor signals, medical device data, electronic health records, imaging data, and other real-world clinical datasets. You will work in a highly cross-functional environment with algorithm engineers, data scientists, clinicians, systems engineers, software teams, regulatory specialists, and product leaders to translate complex clinical and physiological problems into validated, deployable algorithmic solutions.

This is a senior technical role suited for an engineer with deep expertise in classical machine learning, modern AI/deep learning, physiological signal interpretation, and real-world deployment across both cloud-based platforms and edge medical devices.

Key Responsibilities

• Lead the design, development, validation and deployment of AI/ML algorithms for continuous physiological monitoring, novel vital sign and physiological parameter development, disease detection and risk stratification and early warning systems.
• Develop algorithms using multi-modal data sources, including physiological waveforms and time-series data such as PPG, blood pressure, capnography and other monitoring signals, bedside and wearable sensor data, electronic health records and structured clinical data, etc.
• Apply advanced analytical approaches, including:
• Classical machine learning models
• Statistical modeling and probabilistic inference
• Time-series modeling
• Signal processing and feature engineering
• Deep learning architectures
• Multimodal AI models
• Transformer-based models and foundation-model approaches where appropriate
• Translate clinical and physiological understanding into robust algorithm design, including feature selection, model architecture, performance targets, and clinically interpretable outputs.
• Build end-to-end algorithm 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 ensure algorithms are developed with appropriate 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.

Required Qualifications

• 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 both classical and modern approaches.
• Strong hands-on experience with physiological time-series data, biomedical signals, medical device data, or clinical datasets.
• Understanding of human physiology, especially as it relates to patient monitoring.
• Demonstrated experience developing algorithms from early feasibility through productization or deployment.
• Experience working with multi-modal healthcare data, such as 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, TensorFlow, scikit-learn, or equivalent tools.
• Experience with cloud-based algorithm development and deployment using platforms such as Azure, AWS, or GCP.
• Experience optimizing models for deployment on 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.

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