Senior Scientist, Physical AI for Autonomous Discovery

Johnson & Johnson Co.; Cambridge (MA); Sur place; USD 137 000 - 236 000; Plein temps

Avantages offerts par ce poste

Medical insurance

Dental insurance

Vision insurance

401(k)

Paid time off

Résumé du poste

Johnson & Johnson is recruiting a Senior Principal Investigator, Physical AI for Autonomous Discovery to join our In Silico Drug Discovery team. Preferred location is Spring House, PA and consideration for Cambridge, MA; no remote option.

The role leads hands-on development of Physical AI capabilities for autonomous laboratory experimentation, combining AI, robotics, and scientific reasoning to improve outcomes.

Qualifications

• PhD in artificial intelligence, machine learning, robotics, computer science, engineering, computational biology, or related field.
• Demonstrated track record of independently developing AI systems in complex environments.
• Strong software development skills and reproducible deployment practices.

Responsabilités

• Lead design, development, and validation of AI models for lab instrumentation and state estimation.
• Develop adaptive experimental optimization methods using active learning and Bayesian optimization.
• Implement anomaly detection, recovery actions, and safe autonomous decision-making.
• Mentor colleagues and collaborate across multidisciplinary teams.

Connaissances

World models

Reinforcement learning

Bayesian optimization

Multimodal AI

Robotics

Python

AI systems deployment

Experiment design

Formation

PhD in AI/ML/Robotics

Research/industry experience

Outils

Python

ML frameworks

Experiment software

Description du poste

Johnson & Johnson is recruiting a Senior Principal Investigator, Physical AI for Autonomous Discovery to join our In Silico Drug Discovery team. Preferred location is Spring House, PA and consideration for Cambridge, MA; no remote option.

The role leads hands-on development of Physical AI capabilities for autonomous laboratory experimentation, combining AI, robotics, and scientific reasoning to improve outcomes.