Senior ML Engineer for Biologics Discovery

Johnson Johnson; Raritan (NJ); On-site; USD 109,000 - 175,000; Full time

Job summary

Johnson & Johnson Innovative Medicine seeks a Senior ML Engineer for the Biologics Discovery Data Science team. This role builds and operates the integration, deployment, lifecycle management, and governance capabilities enabling ML solutions to run reliably in discovery environments. Based in Spring House, PA (preferred), Titusville, NJ, or Raritan, NJ; Beerse, Belgium or Madrid, Spain.

No remote option. You will partner with data scientists and discovery scientists to operationalize model data

Qualifications

• Degree in Computer Science, Engineering, Data Science, ML, or related field.
• 4+ years operationalizing and scaling AI/ML in production.
• Strong Python proficiency for ML workflows.
• Experience with cloud infra and modern data platforms.

Responsibilities

• Build and operate scalable pipelines for model-ready data to ML and agentic workflows.
• Enable closed-loop learning by ensuring data can be captured and governed downstream.
• Establish reliable deployment, serving, monitoring, access management, and lifecycle across envs.
• Implement versioning, reproducible releases, rollback, and traceability across the ML lifecycle.

Skills

Python

CI/CD

Cloud platforms

ML lifecycle

MLflow

Weights & Biases

Kubernetes

Model registries

Data pipelines

Education

Bachelor's degree in Computer Science, Engineering, Data Science, Machine Learning, or related field

Tools

MLflow

Weights & Biases

Kubernetes

CI/CD

Job description

Johnson & Johnson Innovative Medicine seeks a Senior ML Engineer for the Biologics Discovery Data Science team. This role builds and operates the integration, deployment, lifecycle management, and governance capabilities enabling ML solutions to run reliably in discovery environments. Based in Spring House, PA (preferred), Titusville, NJ, or Raritan, NJ; Beerse, Belgium or Madrid, Spain.

No remote option. You will partner with data scientists and discovery scientists to operationalize model data