Lead AI Scientist: Multimodal Reasoning for Drug Discovery

Johnson Johnson; Cambridge (MA); On-site; USD 117,000 - 201,000; Full time

Benefits offered by this job

Vacation – 120 hours/yr

Sick time – 40 hours/yr

Holiday pay – 13 days/yr

Parental Leave – 480 hours/yr

Bereavement Leave – 240 hours/yr

Job summary

Johnson & Johnson Innovative Medicine is hiring a Principal Scientist to architect an AI-Reasoning platform for translational safety in drug discovery. You will work on multimodal data across discovery, preclinical, and clinical domains, leveraging LLMs and agentic systems to accelerate therapeutics from hypothesis to trials.

The role emphasizes leading research, advancing uncertainty quantification, and shaping the future of trustworthy AI in pharma with collaborations across teams at Spring

Qualifications

• Ph.D. in AI/ML or related field with 2+ years of proven experience.
• Deep expertise in Bayesian inference, uncertainty quantification, information theory, causal inference, graph neural networks.
• Proficiency with agentic frameworks (LangGraph, DSPy, mem0) and DL platforms (PyTorch, TensorFlow).
• Strong Python programming skills and scalable solution design.
• Experience leading small research teams.

Responsibilities

• Pioneer multimodal AI systems that synthesize data into insights for drug discovery.
• Architect memory systems for AI that retain knowledge and support context-aware decisions.
• Define reliability standards for AI in R&D and assess uncertainty.
• Mentor teams to push AI/ML frontiers in drug discovery.
• Benchmark AI agents against experts and publish findings when appropriate.

Skills

Bayesian inference

uncertainty quantification

causal inference

graph neural networks

information theory

agentic reasoning

Python

leadership

Education

Ph.D. in AI/ML or related fields

Tools

LangGraph

DSPy

mem0

PyTorch

TensorFlow

Job description

Johnson & Johnson Innovative Medicine is hiring a Principal Scientist to architect an AI-Reasoning platform for translational safety in drug discovery. You will work on multimodal data across discovery, preclinical, and clinical domains, leveraging LLMs and agentic systems to accelerate therapeutics from hypothesis to trials.

The role emphasizes leading research, advancing uncertainty quantification, and shaping the future of trustworthy AI in pharma with collaborations across teams at Spring