This role pays less than
81%
of similar roles. Most pay
$174,600–$260,085
— the shaded band above.
At the midpoint, this role pays about
$159k
versus about
$217k
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
71 open roles
on FindRole.
Listed pay typically runs
$109,000–$177,100
across 65 roles with salary data.
The Principal Scientist – AI-Reasoning for Drug Discovery joins the DDSAI-OCMO team to architect an AI-reasoning platform supporting translational safety decision-making in drug discovery and development. This role involves pioneering multimodal AI systems that synthesize scientific data into actionable insights, developing agent memory systems for context-aware decisions, and researching uncertainty quantification to ensure trustworthy reasoning. The position focuses on leveraging Large Language Models (LLMs) and agentic systems to process datasets spanning scientific literature, omics, biomolecular knowledge, and manufacturing processes. Key technical requirements include expertise in Bayesian inference, information theory, causal inference, graph neural networks, and Python programming. Candidates will utilize tools such as LangGraph, DSPy, mem0, PyTorch, and TensorFlow. The work addresses the challenge of accelerating drug discovery timelines by automating complex reasoning across preclinical, clinical, and real-world evidence domains to move therapeutics toward clinical trials.
What does a Scientist earn?
Median $141900 from 33 postings across 8 companies.
Develop multimodal AI systems that synthesize scientific data into actionable insights to accelerate drug discovery.
Architect next-generation agent memory systems to ensure AI retains knowledge and makes context-aware decisions.
Conduct research in uncertainty quantification to establish standards for trustworthy, reliable AI-driven scientific reasoning.
Translate emerging AI/ML frontiers into practical applications while mentoring teams in computational drug discovery.
Benchmark AI agents against human experts and state-of-the-art methods to validate scientific impact.
Build scalable Python solutions that bridge the gap between innovative research and production deployment.
What we're looking for
Ph.D. in AI/ML or related fields such as computer science, machine learning, data science, applied mathematics, or statistics.
At least 2 years of proven experience in the field.
Deep expertise in mathematical frameworks including Bayesian inference, uncertainty quantification, information theory, causal inference, and graph neural networks.
Proficiency with agentic frameworks like LangGraph, DSPy, and mem0, and deep learning platforms such as PyTorch or TensorFlow.
Exceptional Python programming skills for architecting scalable solutions.
Ability to lead small teams of researchers.
Understanding of the drug discovery pipeline and familiarity with biological data types (preferred).
Strong understanding of statistics and applicable methods to validate model performance (preferred).
Generative AI
Large Language Models
LLMOps
MLOps
Python
PyTorch
TensorFlow
Hugging Face
Azure AI
Azure OpenAI
Azure Machine Learning
Semantic Kernel
AutoGen
LangGraph
CI/CD
Retrieval Systems
Agentic Systems
Statistical Modeling
Deep Learning
Machine Learning
BioNeMo
RNA Therapeutics
Computational Biology
Data Analysis
Automation
Platform Engineering
Active Learning
Foundation Models
Simulation
Data Lineage
Feature Stores
Drug Discovery
Assay Development