Director, Physical AI for Autonomous Discovery

Role Summary

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.

Key Responsibilities

• 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.

What Success Looks Like

• 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 optimization improves experimental quality, throughput, reproducibility, and scientific decision-making.
• Autonomous capabilities progress safely from AI-assisted automation to self-improving discovery workflows.
• Work closely with J&J IT and Data, Data Science & AI partners to deliver a robust and secure agentic platform.

Key Skills

• 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.

Qualifications

• 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

• Self-driving laboratories, laboratory automation, or closed-loop scientific experimentation.
• Vision-language-action models, digital twins, machine vision, robotic control, or multi-agent systems.
• Biologics, drug discovery, life sciences, or another complex experimental domain.
• Deployment of safety-critical or high-reliability AI systems with monitoring and governance controls.