below marketmost similar roles pay hereabove market
This role pays less than
85%
of similar roles. Most pay
$196,750–$257,462
— the blue band above.
At the midpoint, this role pays about
$186k
versus about
$227k
for comparable roles.
Based on 240 similar postings.
Employer
About Johnson & Johnson
Johnson & Johnson is a multinational corporation operating in three main segments: consumer health products, pharmaceuticals, and medical devices, known for brands like Tylenol, Band-Aid, and Janssen. Industry: Pharmaceuticals & Medical Devices
Johnson & Johnson currently has
46 open roles
on FindRole.
Listed pay typically runs
$113,000–$201,250
across 38 roles with salary data.
The Senior Principal Investigator, Physical AI for Autonomous Discovery joins the In Silico Drug Discovery team to serve as a hands-on scientific and technical leader. This individual-contributor role focuses on developing Physical AI capabilities for autonomous laboratory experimentation within the drug discovery domain. The successful candidate will design, implement, and validate systems that perceive laboratory states, recommend actions, and learn from experimental data to improve outcomes. Key responsibilities include developing AI models for instruments and reagents, implementing adaptive optimization methods, and building agentic capabilities for experiment planning. Required expertise includes Python, modern AI/ML frameworks, and robotics. Core competencies involve active learning, Bayesian optimization, reinforcement learning, and multi-agent coordination. The role solves the challenge of automating complex laboratory workflows while ensuring error detection, causal reasoning, and uncertainty quantification.
Build models to generate testable hypotheses for experimental failures and recommend follow-up experiments.
Develop agentic and multi-agent capabilities to support experiment planning and execution monitoring.
Translate scientific needs into technical requirements and production-ready prototypes in partnership with engineering and data science teams.
Embed uncertainty calibration, safety constraints, monitoring, and governance into autonomous decision-making systems.
Provide technical guidance and establish scientific best practices to mentor colleagues.
What we're looking for
PhD in artificial intelligence, machine learning, robotics, computer science, engineering, computational biology, or a related quantitative field.
Substantial relevant research or industry experience in developing and deploying AI systems in physical or scientific environments.
Deep expertise in world models, reinforcement learning, Bayesian optimization, causal inference, multimodal AI, robotics, or scientific machine learning.
Advanced software development skills in Python, modern machine-learning frameworks, experimental software design, testing, and reproducible deployment.
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 across multidisciplinary teams in a matrix environment.
Experience in self-driving laboratories, laboratory automation, or closed-loop scientific experimentation (preferred).
Experience with vision-language-action models, digital twins, machine vision, robotic control, or multi-agent systems (preferred).
Robotics
Autonomous Vehicles
Physical AI
Deep Learning
Reinforcement Learning
Python
PyTorch
C++
CUDA
Parallel Programming
Simulation
Embodied AI
Machine Learning
Verification and Validation
Closed-loop Training
Python
C++
PyTorch
CUDA
Reinforcement Learning
Deep Learning
Robotics
Autonomous Vehicles
Physical AI
Parallel Programming
Simulation
Closed-loop Training
Verification and Validation
Embodied AI
Multimodal Foundation Models
LLMs
Large Vision-Language Models
Diffusion Algorithms
Action-based Transformers
PyTorch
Jax
TensorFlow
Python
C++
CUDA
Reinforcement Learning
Imitation Learning
ROS
Robot Kinematics
Model Predictive Control
MuJoCo
Isaac Sim
Whole-body Control