Scientific Fellow, Ai Safety, R&d Data Science And Digital Health
Johnson & Johnson Innovative Medicine (IM)
New Brunswick, NJ, USA
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Ai safety, robustness, and observability
Foundation and predictive ai models
Generative ai and autonomous agentic systems
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Johnson & Johnson is seeking a Scientific Fellow in AI Safety for their R&D Data Science and Digital Health organization. The role focuses on embedding safety protocols into advanced AI systems, requiring strong technical leadership and experience in AI safety within healthcare contexts.
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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: 75
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Johnson & Johnson is seeking a Scientific Fellow in AI Safety for their R&D Data Science and Digital Health organization. The role focuses on embedding safety protocols into advanced AI systems, requiring strong technical leadership and experience in AI safety within healthcare contexts.
**
Skills & Requirements
Must-have
AI safety, robustness, and observability
foundation and predictive AI models
generative AI and autonomous agentic systems
AI safety-by-design principles
safety-focused models and evaluations
Nice-to-have
technical leadership for AI safety in regulated environment
external ambassador for J&J IM R&D AI safety
sustained mentorship program for AI safety
Key Requirements
PhD or equivalent advanced degree
Minimum of 10 years of post-academic, industry experience
Proven track record with modern AI systems
Extensive experience with AI safety, robustness, reliability, or evaluation
Experience working in highly interdisciplinary and matrixed environments