Senior Machine Learning Engineer, Biologics Discovery

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.

Key Responsibilities

AI/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 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.

Qualifications

Required

• Degree in Computer Science, Engineering, Data Science, Machine Learning, or a related computational field.
• 4+ years of experience operationalizing and scaling AI/ML solutions in production environments, including ML, generative AI, or agentic workflows.
• Strong proficiency in Python, with experience developing AI/ML workflows for model training, fine-tuning, evaluation, deployment, and serving.
• Experience with cloud infrastructure and modern data platforms used to support AI/ML workloads.
• Expertise with model registries, experiment tracking, and ML lifecycle management tools (e.g., MLflow, Weights & Biases).
• Experience implementing production AI/ML practices, including model versioning, deployment automation, CI/CD, automated testing, observability, monitoring, containers, orchestration technologies, and scalable compute environments.
• Strong software development and automation practices, with the ability to partner effectively with data scientists, AI/ML practitioners, technology teams, and domain experts.

Preferred

• Experience in pharmaceutical, biotechnology, or life sciences sectors.
• Exposure to real-time/near-real-time pipelines and instrument data integration.
• Experience working with FAIR data principles, metadata management, data lineage, provenance, and AI-ready data practices.