Ingeniero de ML Sénior, Biologics Discovery

R&D Product Development

R&D Machine Learning

Scientific/Technology

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.

This position will be based at one of our office locations in either Spring House, PA (strongly preferred), Titusville, NJ, or Raritan, NJ, USA; Beers, BE, or Madrid, Spain. (No remote option.)

Please note that this role is available across multiple countries and may be posted under different requisition numbers to comply with local requirements. While you are welcome to apply to any or all of the postings, we recommend focusing on the specific country(s) that align with your preferred location(s):

USA - Requisition Number: R-099962

Spain - Requisition Number: R-100646

Belgium - Requisition Number: R-100647

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.

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.

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.

The ideal candidate is a pragmatic AI/ML practitioner who combines operational expertise with a passion for reliability, reproducibility, and automation. They thrive at bridging scientific needs and technical capabilities, enabling AI/ML solutions to move efficiently from experimentation to trusted scientific impact.

• 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 management, and operational excellence across AI/ML solutions.
• Enable AI capabilities to scale with growing scientific data volumes, computational demands, and increasingly autonomous discovery workflows.
• Monitor model and system behavior in production, including data quality, model performance, drift, latency, reliability, and resource utilization.

Partnership & Standards

• Partner with data scientists, technology teams, and domain experts to establish reliable integration patterns between scientific data products and AI/ML workflows.
• Enable ML scientists and AI agents with reproducible training, fine-tuning, evaluation, experimentation, and deployment capabilities.
• Establish reusable patterns, best practices, and standards that accelerate the transition from experimentation to production deployment.
• Contribute to security, access control, AI governance, documentation, and cost management practices across AI/ML solutions.

Madrid, Spain

This position will be based at one of our office locations in either Spring House, PA (strongly preferred), Titusville, NJ, or Raritan, NJ, USA; Beerse, BE, or Madrid, Spain. (No remote option.)

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