Director, Physical AI Autonomous Discovery

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, and solutions are personal.

Through our expertise in Innovative Medicine and Med Tech, 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.

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Job Function:
Data Analytics & Computational Sciences Job Sub Function:
Data Science Job Category:
People Leader All Job Posting Locations:
Cambridge, Massachusetts, United States of America, Spring House, Pennsylvania, 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. 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 Director, Physical AI for Autonomous Discovery to join our In Silico Drug Discovery team. The preferred location for this position is Spring House, PA (Philadelphia area) and may require travel as needed. Consideration may be given for candidates near the Cambridge, MA area. (No remote option.)

The Director, Physical AI for Autonomous Discovery is a key leader who will define the Physical AI strategy and lead a focused team developing models that enable autonomous laboratory experimentation. This role combines AI, robotics, laboratory automation, and scientific reasoning to create systems that perceive, reason, act, learn, recover from errors, and continuously improve experimental outcomes.

• Define and execute the Physical AI strategy, build and lead a high-performing team, and establish a roadmap aligned with scientific priorities and business objectives.
• Develop AI models that understand and reason about instruments, samples, reagents, robotic systems, workflows, and laboratory state.
• Build adaptive experimental optimization capabilities using active learning, Bayesian optimization, reinforcement learning, and multi-objective optimization.
• Create systems for physical error detection and correction, including anomaly recognition, root‑cause assessment, workflow replanning, recovery actions, and appropriate human escalation.
• Develop models that generate hypotheses for experimental failures, unexpected results, variability, and irreproducibility, and recommend discriminating follow‑up experiments.
• Design agentic and multi‑agent systems that coordinate experiment planning, execution monitoring, data interpretation, and decision‑making across Design‑Make‑Test‑Analyze cycles.
• Partner with automation, robotics, data science, engineering, and discovery scientists to deploy reliable closed‑loop learning in real laboratory environments.
• Embed calibrated uncertainty, safety constraints, monitoring, traceability, and governance into autonomous decision‑making.

• Laboratory systems detect, diagnose, and recover from execution failures with fewer unplanned interventions.
• AI‑generated hypotheses explain experimental anomalies and guide informative follow‑up experiments.
• Closed‑loop…