Director, Physical AI – Autonomous Discovery

Johnson & Johnson

. Define and execute the Physical AI strategy .

Posted 10/16/2026full-timeUnited StatesLead💰 $164,000 - $282,900 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in developing AI models for physical and scientific environments, with a strong focus on reinforcement learning, Bayesian optimization, and multi-agent systems. Proven ability to lead teams and collaborate across disciplines to deliver scalable, reliable AI solutions in laboratory settings.

Highest-signal resume keywords

PhD In Artificial IntelligenceReinforcement LearningBayesian OptimizationPython ProgrammingAI Systems Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Reinforcement LearningBayesian OptimizationCausal InferenceMultimodal AIRoboticsScientific Machine LearningAI Model DevelopmentExperimental OptimizationAnomaly DetectionHypothesis Generation

Soft Skills

CollaborationCommunicationTeam Leadership

Tools & Technologies

Machine Learning FrameworksRobotic ControlDigital TwinsVision-Language-Action ModelsClosed-Loop Scientific Experimentation

Industry Keywords

Self-Driving LaboratoriesLaboratory AutomationBiologicsDrug DiscoveryLife Sciences

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Define and execute the Physical AI strategy
Build and lead a high-performing team
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
Recommend discriminating follow-up experiments
Design agentic and multi-agent systems coordinating 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
Work closely with J&J IT and Data, Data Science & AI partners to deliver a robust and secure agentic platform

Requirements

What you’ll need

PhD in artificial intelligence, machine learning, robotics, computer science, engineering, computational biology, or a related quantitative field
Demonstrated experience developing AI systems that operate in physical or scientific environments
Deep expertise in several relevant areas, such as world models, reinforcement learning, Bayesian optimization, causal inference, multimodal AI, robotics, or scientific machine learning
Strong software-development skills, including Python and modern machine-learning frameworks
Ability to translate scientific and operational requirements into robust, scalable AI models and to communicate across multidisciplinary teams
Strong collaboration skills to work in matrix environment
Preferred experience with self-driving laboratories, laboratory automation, or closed-loop scientific experimentation
Preferred experience with vision-language-action models, digital twins, machine vision, robotic control, or multi-agent systems
Preferred experience in biologics, drug discovery, life sciences, or another complex experimental domain
Preferred experience deploying safety-critical or high-reliability AI systems with monitoring and governance controls
May require travel as needed

Benefits

Comp & perks

Annual performance bonus
Medical insurance
Dental insurance
Vision insurance
Life insurance
Short- and long-term disability insurance
Business accident insurance
Group legal insurance
Consolidated retirement plan (pension)
Savings plan (401(k))
Vacation – up to 120 hours per calendar year
Sick time – up to 40 hours per calendar year
Holiday pay, including Floating Holidays – up to 13 days per calendar year
Work, Personal and Family Time – up to 40 hours per calendar year