Senior Principal Investigator, Physical AI Autonomous Discovery

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world 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:
Scientific/Technology All Job Posting Locations:
Cambridge, Massachusetts, United States of America, 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 Senior Principal Investigator, 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.) Reporting to the Director, Physical AI for Autonomous Discovery, the Senior Principal Investigator will serve as a hands-on scientific and technical leader developing Physical AI capabilities for autonomous laboratory experimentation.

This individual-contributor role combines AI, robotics, laboratory automation, and scientific reasoning to design, implement, and validate systems that perceive laboratory state, recommend actions, learn from experimental data, recover from errors, and improve experimental outcomes.

• Lead the hands-on design, development, and validation of AI models that represent instruments, samples, reagents, robotic systems, workflows, and laboratory state.
• Develop and evaluate adaptive experimental optimization methods using active learning, Bayesian optimization, reinforcement learning, and multi-objective optimization.
• Implement capabilities for physical error detection and correction, including anomaly recognition, root-cause assessment, workflow replanning, recovery actions, and appropriate human escalation.
• Build models that generate testable hypotheses for experimental failures, unexpected results, variability, and irreproducibility, and recommend discriminating follow-up experiments.
• Develop agentic and multi-agent capabilities that support experiment planning and execution monitoring across Design-Make-Test-Analyze cycles.
• Translate scientific and operational needs into technical requirements, prototypes, and production-ready capabilities in partnership with automation, robotics, data science, engineering, and discovery scientists.
• Provide technical guidance, establish scientific best practices, and mentor colleagues without formal people-management responsibility.
• Embed calibrated uncertainty, safety constraints, monitoring, traceability, and governance into autonomous decision-making, escalating strategic tradeoffs to the Director as appropriate.

• Physical AI & World Models:
  Embodied AI, multimodal perception, state estimation, digital twins, and predictive models of laboratory systems.
• Autonomous Experimentation:
  Active learning, Bayesian optimization, sequential design, reinforcement learning, and multi-objective optimization.
• Error Intelligence:
  Anomaly detection, fault diagnosis, causal reasoning, hypothesis generation, recovery planning, and uncertainty quantification.
• Robotics & Automation:
  Laboratory robotics, liquid handling, sensors, machine vision, orchestration, scheduling, and instrument integration.
• Agentic AI:
  Scientific agents, multi-agent coordination, planning, tool use, memory, guardrails, and human-on-the-loop controls.
• Scientific Software:
  Python and modern AI/ML frameworks; production model deployment, APIs, data pipelines, observability, and MLOps.

Prototype and production capabilities measurably reduce unplanned intervention by detecting, diagnosing, and supporting recovery from laboratory execution…