Senior Principal Investigator, Physical AI for Autonomous Discovery

Johnson & Johnson•Spring House, PA

20h•Onsite

About The Position

Johnson & Johnson Innovative Medicine is recruiting a Senior Principal Investigator for its In Silico Drug Discovery team. This hands-on individual-contributor role combines AI, robotics, laboratory automation, and scientific reasoning to develop Physical AI capabilities for autonomous laboratory experimentation. The successful candidate will design, implement, and validate systems that perceive laboratory state, recommend actions, learn from experimental data, recover from errors, and improve experimental outcomes. The preferred location is Spring House, Pennsylvania, with consideration for candidates near Cambridge, Massachusetts; the position has no remote option and may require travel as needed. The role includes developing AI models representing instruments, samples, reagents, robotic systems, workflows, and laboratory state; creating adaptive experimental optimization and error-recovery capabilities; and building systems that generate hypotheses and recommend follow-up experiments. The Senior Principal Investigator will develop agentic and multi-agent capabilities for experiment planning and monitoring across Design-Make-Test-Analyze cycles, and translate scientific and operational needs into technical requirements, prototypes, and production-ready solutions with cross-functional teams. The position provides technical guidance and mentorship without formal people-management responsibility. The individual will embed uncertainty calibration, safety constraints, monitoring, traceability, and governance into autonomous decision-making, and escalate strategic tradeoffs to the Director as appropriate. Success includes reducing unplanned laboratory intervention, improving data quality and learning efficiency, validating closed-loop optimization against scientific and operational benchmarks, and advancing reliable, secure, governed capabilities from proof of concept to deployment.

Requirements

PhD in artificial intelligence, machine learning, robotics, computer science, engineering, computational biology, or a related quantitative field, with 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.

Nice To Haves

Experience with self-driving laboratories, laboratory automation, or closed-loop scientific experimentation.
Experience with vision-language-action models, digital twins, machine vision, robotic control, or multi-agent systems.
Experience in biologics, drug discovery, life sciences, or another complex experimental domain.
Experience deploying safety-critical or high-reliability AI systems with monitoring and governance controls.

Responsibilities

Lead hands-on design, development, and validation of AI models representing laboratory instruments, samples, reagents, robotic systems, workflows, and state.
Develop and evaluate adaptive experimental optimization methods, including 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 appropriate human escalation.
Build models that generate testable hypotheses about experimental failures, unexpected results, variability, and irreproducibility, and recommend discriminating 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 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; escalate strategic tradeoffs to the Director as appropriate.
Partner with the Director and J&J IT and Data, Data Science & AI, automation, and scientific teams to deliver robust, secure, and governed agentic capabilities.
Advance technical workstreams from proof of concept to reliable deployment through documented designs, reproducible evaluation, and cross-functional execution.

Benefits

Annual performance bonus, subject to the applicable plan and company discretion.
Medical insurance.
Dental insurance.
Vision insurance.
Life insurance.
Short- and long-term disability insurance.
Business accident insurance.
Group legal insurance.
Potential eligibility to participate in the consolidated retirement plan (pension) and 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.