Scientific Fellow, Ai Safety, R&d Data Science And Digital Health
Johnson & Johnson
New Brunswick, NJ, United States of America
$196,000.00 - $342,700.00 py
On-site (with up to 25% travel)
Ai safety principles
Generative ai
Agentic systems
Johnson & Johnson is seeking a Scientific Fellow in AI Safety within their R&D Data Science and Digital Health division. The role focuses on ensuring AI systems are safe and trustworthy across various healthcare applications, requiring a strong technical background in AI and machine learning
Job Summary
This role is responsible for embedding AI safety, robustness, and observability into the design, evaluation, and deployment of advanced AI systems across the DSDH portfolio and R&D use cases.
The Scientific Fellow will work closely with AI scientists, engineers, AI Quality & Optimization, Global Regulatory Affairs, Quantitative Scientists, and Johnson & Johnson Technology (JJT) to ensure AI systems deployed in R&D workflows are safe, trustworthy, and fit‑for‑purpose as AI capability and autonomy scale.
Drive J&J innovation in the field, leading to high visibility publications in top-tier AI conferences and journals, patents around AI safety in generative AI, reasoning, multi-agent systems, etc.
Matching Summary
Match Score: 85
Johnson & Johnson is seeking a Scientific Fellow in AI Safety within their R&D Data Science and Digital Health division. The role focuses on ensuring AI systems are safe and trustworthy across various healthcare applications, requiring a strong technical background in AI and machine learning.
Salary
$196,000.00 - $342,700.00
Skills & Requirements
Must-have
AI safety principles
Generative AI
Agentic systems
Foundation models
Failure modes and risk analysis
Safety-focused evaluations
Nice-to-have
Cross-industry collaboration
External ambassadorship
Mentorship program development
Key Requirements
PhD or equivalent advanced degree
10+ years of post-academic industry experience
Proven track record with modern AI systems
Extensive experience with AI safety, robustness, or reliability
Ability to reason about system-level behavior and risk