Artista / Director Técnico De Machine Learning – Vfx

At Johnson & Johnson,we believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented, treated, and cured,where treatments are smarter and less invasive, andsolutions are personal.Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity.Learn more at jnj.Com.

As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Job Function

R&D Product Development

Job Sub Function

R&D Machine Learning

Job Category

Scientific/Technology

All Job Posting Locations

Beerse, Antwerp, Belgium, Madrid, Spain, Raritan, New Jersey, United States of America, Spring House, Pennsylvania, United States of America, Titusville, New Jersey, United States of America

Job Description

Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.

Learn more at

About The Opportunity

Johnson & Johnson Innovative Medicine is seeking a Senior ML Engineer for our Biologics Discovery Data Science team. This role builds and operates the integration, deployment, lifecycle management, and governance capabilities that enable machine learning (ML) models and AI solutions developed by partner organizations to run reliably in Biologics Discovery environments. You are the senior team member who closes the gap between model-ready data in our data warehouse and models that serve discovery scientists.

we recommend focusing on the specific country(s) that align with your preferred location(s):

USA - Requisition Number: R-

Spain - Requisition Number: R-

Belgium - Requisition Number: R-

Why This Role Matters

The future of AI-native discovery depends on high-quality AI and data solutions that connect scientific data, machine learning models, and agentic workflows. This role will shape how enterprise AI and MLOps capabilities are adapted, integrated, and operationalized for Biologics Discovery, enabling AI solutions to scale from prototypes into trusted capabilities that accelerate scientific learning and therapeutic discovery.

Position Summary

In this role, you will enable AI/ML solutions to move reliably from development into production within Biologics Discovery. Working closely with data scientists, AI/ML scientists, discovery scientists, and partner organizations at J&J, you will own the deployment, lifecycle management, access, monitoring, and governance of ML, generative AI, and agentic solutions for discovery workflows. The role does not own core model development or the underlying enterprise platforms. Instead, it ensures that models and AI capabilities developed by partner teams are operationalized reliably for scientific use.

You will bring expertise in modern AI/ML operational practices, including reproducibility, CI/CD, observability, governance, automation, and scalable compute, helping adapt enterprise capabilities for discovery-specific use cases. Your work will enable reliable, production-grade AI workflows and accelerate the adoption of ML and agentic systems in biologics discovery.

Why This Role Is Unique

This is a rare opportunity to play a key role in enabling AI-native Biologics Discovery. You will help operationalize and scale AI/ML capabilities that transform model-ready data and promising models into reliable, production-grade solutions that accelerate scientific discovery.

Key ResponsibilitiesAI/ML Operations & Lifecycle Management

• Build and operate scalable pipelines and interfaces that deliver model-ready data to ML, generative AI, and agentic workflows.
• Enable closed-loop scientific learning by ensuring newly generated scientific data can be captured, governed, and made available to downstream modeling, evaluation, and agentic workflows.
• Establish reliable operational capabilities for model deployment, serving, monitoring, access management, and lifecycle management across development and production environments.
• Implement model, data, and workflow versioning, with reproducible releases, rollback capabilities, and traceability across the AI/ML lifecycle.

Reliability, Observability & Scale

• Establish monitoring, observability, alerting, and performance management practices for ML workflows, deployed models, and AI services.
• Develop and maintain automated workflows supporting testing, release management, environment manag