Director, Physical AI for Autonomous Discovery

Janssen-Cilag Limited; Cambridge (MA); In loco; USD 230.000 - 350.000; Tempo pieno

Descrizione del lavoro

Johnson & Johnson Innovative Medicine invites applications for a Director, Physical AI for Autonomous Discovery within the In Silico Drug Discovery team. The preferred location is Spring House, PA, with potential travel and consideration for candidates near Cambridge, MA.

No remote option. The role defines the Physical AI strategy, leading a team that builds models enabling autonomous laboratory experimentation, combining AI, robotics, and scientific reasoning to advance lab-to-life

Competenze

• Experience leading AI-driven innovation in life sciences.
• Strong background in autonomous laboratory systems and scientific reasoning.
• Ability to translate complex experiments into autonomous workflows.

Mansioni

• Define and execute Physical AI strategy; build and lead a high-performing team.
• Develop AI models for instruments, samples, reagents, robotics, and workflows.
• Create adaptive optimization using active learning, Bayesian methods, RL, and multi-objective optimization.
• Design agentic systems coordinating planning, execution, data interpretation, and decision-making.

Conoscenze

AI

Robotics

Lab automation

Reinforcement learning

Strumenti

Python

Descrizione del lavoro

At Johnson & Johnson,we believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented, treated, and cured,where treatments are smarter and less invasive, andsolutions are personal.Through our expertise in Innovative Medicine and MedTech, 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.Learn more at jnj.com .

As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Job Function:
Data Analytics & Computational Sciences

Job Sub Function:
Data Science

Job Category:
People Leader

All Job Posting Locations:
Cambridge, Massachusetts, United States of America, Spring House, Pennsylvania, United States of America

Job Description:

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 https://www.jnj.com/innovative-medicine

Johnson & Johnson Innovative Medicine is recruiting for a Director, 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.)

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-im