Staff ML Engineer: Forecasting & Production AI

Becton, Dickinson and Company (BD); Irvine (CA); On-site; USD 161,000 - 258,000; Full time

Job summary

BD is seeking a Staff Machine Learning Engineer to lead the design, development, and deployment of ML solutions powering AI-driven decisions across the BD Connected Care portfolio. You will build and operationalize forecasting, optimization, anomaly detection, and other advanced analytics use cases.

This is a hands-on technical leadership role owning the end-to-end ML lifecycle from experimentation to deployment, shaping architecture and best practices while collaborating with engineering,

Qualifications

• Bachelor's degree in ML, Stats, OR, CS, or related field.
• 7+ years of hands-on ML/AI with production ownership and forecasting/optimization systems.
• Deep expertise in classical ML and deep learning, including time-series.

Responsibilities

• Own the design, training, and validation of forecasting, optimization, and anomaly-detection models across data.
• Apply classical methods (ARIMA/ETS/state-space) and deep learning approaches, selecting based on problem.
• Own training pipelines end-to-end with feature engineering and reproducibility.
• Turn models into production services on AWS (EKS, S3, Lambda, EventBridge, Step Functions).
• Implement evaluation/monitoring for quality, latency, and drift; collaborate with ML Platform.

Skills

Forecasting

Optimization

Time-series modelling

Python

AWS

CI/CD

Mentorship

Education

Bachelor's in CS/ML/related

Master's or PhD preferred

Tools

Docker

Kubernetes

GitHub Actions

Job description

BD is seeking a Staff Machine Learning Engineer to lead the design, development, and deployment of ML solutions powering AI-driven decisions across the BD Connected Care portfolio. You will build and operationalize forecasting, optimization, anomaly detection, and other advanced analytics use cases.

This is a hands-on technical leadership role owning the end-to-end ML lifecycle from experimentation to deployment, shaping architecture and best practices while collaborating with engineering,