Senior Staff AI/ML Algorithm Engineer - Becton Dickinson

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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

We are seeking a highly experienced Senior Staff AI/ML Algorithm 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.