Principal Scientist, AI & Autonomous Discovery Systems PA

Job Function:

Data Analytics & Computational Sciences

Data Science

Scientific/Technology

Spring House, Pennsylvania, United States of America

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.

Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.

Learn more at

Johnson & Johnson Innovative Medicine is recruiting for a Principal Scientist, AI & Autonomous Discovery Systems, to advance AI‑powered drug discovery through human‑guided autonomous and agentic discovery platforms and help accelerate drug discovery by creating AI‑enabled Design‑Make‑Test‑Learn ecosystems that improve scientific decision quality, increase experimental throughput, reduce cycle times, and advance high‑value therapeutic programs. The primary location for this position is Spring House, PA.

(No remote option.)

The Data, Data Science, and AI team develops AI solutions across therapeutic areas, including oncology, cardiovascular and metabolic disorders, immunology, neuroscience, and infectious disease. We are seeking a technical leader at the intersection of AI/ML, drug discovery, laboratory automation, and scientific platform architecture.

This role will help shape the future of AI‑enabled research at Johnson & Johnson by creating intelligent systems that connect models, data, experiments, laboratories, and scientists into continuously learning discovery ecosystems. These capabilities will accelerate the discovery and development of transformational medicines across therapeutic areas.

• Build AI‑enabled discovery workflows that connect target discovery, molecular/property prediction, molecular design, experiment planning, assay optimization, and data interpretation across modalities, therapeutic areas, and functional groups.

• Define the technical vision, long‑term strategy, and scalable multi‑agent architecture for AI‑driven laboratory orchestration and human‑guided autonomous discovery systems, coordinating scientific reasoning, knowledge retrieval, experiment planning, model execution, workflow automation, and decision support while influencing investments across discovery platforms, therapeutic areas, and enterprise AI initiatives.

• Connect AI models, scientific applications, data platforms, laboratory systems, instruments, robotics, cloud services into coherent Design‑Make‑Test‑Learn workflows that continuously learn from experimental outcomes, assay results, and scientific feedback.

• In collaboration with generic agentic system providers, develop intelligent scientific systems for drug discovery and translational research that integrate experimental data, scientific literature, domain knowledge, and predictive multimodal AI models to advance hypothesis generation, experimental design, causal reasoning, active learning, multi‑objective optimization, and scientific decision‑making.

• In collaboration with broader teams, contribute to the establishment of rigorous standards for AI governance, safety, observability, reliability, auditability, data governance, security, and responsible AI, and evaluate systems against scientific, operational, and business outcomes through experimental validation and real‑world deployment.

• Partner with scientists and engineers across drug discovery, data science, automation, and technology organizations to deliver production‑grade AI capabilities.

• Mentor scientists and engineers; communicate technical strategy and results through internal presentations,…