Staff Machine Learning Engineer - Lead ML Solutions

Becton Dickinson NA; Irvine (CA); Presencial; USD 161 000 - 258 000; Tempo integral

Resumo da oferta

BD in Irvine, CA seeks a Staff Machine Learning Engineer to lead end-to-end ML lifecycle—experimentation, training, deployment, monitoring, and optimization—for AI-driven decision support in the Connected Care portfolio.

You will build forecasting and anomaly-detection models using classical and deep-learning methods, collaborate with engineering, product, and clinical teams, and own production-grade ML services on AWS with Docker/Kubernetes.

Qualificações

• Bachelor's in Machine Learning, Statistics, Operations Research, Applied Math, Computer Science, or related field.
• 7+ years of hands-on ML/AI and software engineering with production ownership.
• Deep expertise in model development and training across classical ML and deep learning, including time-series and multi-series settings.
• Excellent Python; fluent with NumPy, pandas, scikit-learn, statsmodels, PyTorch, and forecasting stacks.

Responsabilidades

• Own design, training, and validation of forecasting, optimization, and anomaly-detection models across product, operational, and clinical-adjacent data.
• Apply classical and deep learning methods, including transformer-based architectures, selecting based on the problem.
• Own training pipelines, feature engineering, dataset versioning, and reproducibility.

Conhecimentos

Python

NumPy

pandas

scikit-learn

PyTorch

Forecasting stack

AWS

Docker

Kubernetes

CI/CD

Agile/Scrum

Formação académica

Bachelor's degree in ML/CS/OR related field

Master's or PhD (preferred)

Ferramentas

Prophet

GluonTS

Nixtla

PyTorch Forecasting

Descrição da oferta de emprego

BD in Irvine, CA seeks a Staff Machine Learning Engineer to lead end-to-end ML lifecycle—experimentation, training, deployment, monitoring, and optimization—for AI-driven decision support in the Connected Care portfolio.

You will build forecasting and anomaly-detection models using classical and deep-learning methods, collaborate with engineering, product, and clinical teams, and own production-grade ML services on AWS with Docker/Kubernetes.