Director, Physical AI for Autonomous Discovery

Johnson & Johnson Co.; Spring House (PA); Presencial; USD 164.000 - 283.000; Jornada completa

Descripción de la vacante

Johnson & Johnson is recruiting for a Director, Physical AI for Autonomous Discovery to join the In Silico Drug Discovery team. The preferred location is Spring House, PA, with travel as needed; Cambridge, MA nearby options may be considered. No remote option.

The role leads the Physical AI strategy, combining AI, robotics, and scientific reasoning to enable autonomous laboratory experimentation and self‑improving discovery workflows, partnering across IT, data science, and discovery scientists.

Formación

• 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 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.

Responsabilidades

• 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.

Conocimientos

PhD in AI/ML or related field

World models

Reinforcement learning

Bayesian optimization

Causal inference

Multimodal AI

Robotics / lab automation

Python & ML frameworks

Educación

PhD inAI/ML or related field

Herramientas

Python

ML frameworks

Robotics software

MLOps

Descripción del empleo

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions 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‑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.

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.)

Johnson & Johnson is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or other characteristics protected by federal, state or local law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act.

If you are under 18 years of age, you (the candidate) may need to obtain the necessary working papers or other documentation required by state law to start the assignment, as well as get a parent's consent for the background check.

Johnson & Johnson is committed to providing an interview process that is inclusive of our applicants' needs. If you are an individual with a disability and would like to request an accommodation, external applicants please contact us via https://www.jnj.com/contact-us/careers , internal employees contact AskGS to be directed to your accommodation resource.

The anticipated base pay range for this position is $164,000 to $282,900. The Company maintains highly competitive, performance-based compensation programs. Under current guidelines, this position is eligible for an annual performance bonus in accordance with the terms of the applicable plan. The annual performance bonus is a cash bonus intended to provide an incentive to achieve annual targeted results by rewarding for individual and the corporation's performance over a calendar/performance year. Bonuses are awarded at the Company's discretion on an individual basis. Employees and/or eligible dependents may be eligible to participate in the following Company sponsored employee benefit programs: medical, dental, vision, life insurance, short‑and‑long‑term disability, business accident insurance, and group legal insurance.

Employees may be eligible to participate in the Company's consolidated retirement plan (pension) and savings plan (401(k)).

• 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 of Work, Personal and Family Time - up to 40 hours per calendar year

Additional information can be found through the link below. https://www.careers.jnj.com/employee-benefits

The compensation and benefits information set forth in this posting applies to candidates hired in the United States. Candidates hired outside the United States will be eligible for compensation and benefits in accordance with their local market.

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Preferred Skills:

Advanced Analytics, Budget Management, Compliance Management, Critical Thinking, Data Analysis, Data Privacy Standards, Data Quality, Data Reporting, Data Savvy, Data Science, Data Visualization, Developing Others, Digital Fluency, Inclusive Leadership, Leadership, Program Management, Strategic Thinking, Succession Planning