Senior Principal Investigator, Physical AI – Autonomous Discovery

Johnson & Johnson

. Lead hands-on design, development, and validation of AI models representing instruments, samples, reagents, robotic systems, workflows, and laboratory state .

Posted 10/16/2026full-timeUnited StatesSenior💰 $137,000 - $235,750 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying AI models and systems, with a strong focus on experimental optimization methods and autonomous decision-making. Capable of translating scientific needs into technical solutions while providing mentorship and guidance in a collaborative environment.

Highest-signal resume keywords

PhD In Artificial IntelligenceReinforcement LearningBayesian OptimizationPython ProgrammingExperimental Software Design

ATS Keywords

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Hard Skills

AI Model DevelopmentAdaptive Experimental OptimizationAnomaly RecognitionMulti-Objective OptimizationCausal InferenceMultimodal AIRoboticsScientific Machine LearningTechnical Workstream DefinitionReproducible Deployment Practices

Soft Skills

Strong Communication SkillsCollaboration SkillsInfluence SkillsProblem-Solving Skills

Tools & Technologies

Modern Machine-Learning FrameworksLaboratory AutomationSelf-Driving LaboratoriesVision-Language-Action ModelsDigital TwinsMachine VisionRobotic ControlMulti-Agent Systems

Industry Keywords

BiologicsDrug DiscoveryLife SciencesComplex Experimental DomainSafety-Critical AI SystemsHigh-Reliability AI SystemsMonitoring and Governance Controls

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Lead hands-on design, development, and validation of AI models representing 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 physical error detection and correction, including anomaly recognition, root-cause assessment, workflow replanning, recovery actions, and human escalation
Build models that generate testable hypotheses for experimental failures, unexpected results, variability, and irreproducibility, and recommend follow-up experiments
Develop agentic and multi-agent capabilities supporting experiment planning and execution monitoring across Design-Make-Test-Analyze cycles
Translate scientific and operational needs into technical requirements, prototypes, and production-ready capabilities 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 uncertainty calibration, safety constraints, monitoring, traceability, and governance into autonomous decision-making
Partner with the Director and J&J IT and Data, Data Science & AI, automation, and scientific teams to deliver secure and governed agentic capabilities
Progress technical workstreams from proof of concept to reliable deployment through documented designs, reproducible evaluation, and cross-functional execution

Requirements

What you’ll need

PhD in artificial intelligence, machine learning, robotics, computer science, engineering, computational biology, or a related quantitative field
Substantial relevant research or industry experience
Demonstrated track record of independently developing and deploying AI systems in physical, scientific, or similarly complex environments
Deep expertise in multiple relevant areas, such as world models, reinforcement learning, Bayesian optimization, causal inference, multimodal AI, robotics, or scientific machine learning
Advanced software-development skills, including Python, modern machine-learning frameworks, experimental software design, testing, and reproducible deployment practices
Ability to define technical workstreams, solve ambiguous scientific problems, and deliver robust solutions with limited day-to-day direction
Strong communication, collaboration, and influence skills for working effectively across multidisciplinary teams in a 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

Benefits

Comp & perks

Annual performance bonus eligibility
Medical insurance
Dental insurance
Vision insurance
Life insurance
Short- and long-term disability insurance
Business accident insurance
Group legal insurance
Pension plan
401(k) savings plan
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