Johnson & Johnson Innovative Medicine is seeking a Scientific Fellow in AI Safety to lead the integration of AI safety principles into R&D processes. The role requires a highly experienced technical leader to ensure the safety and robustness of advanced AI systems in healthcare applications, with a focus on research and development
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 Innovative Medicine is seeking a Scientific Fellow in AI Safety to lead the integration of AI safety principles into R&D processes. The role requires a highly experienced technical leader to ensure the safety and robustness of advanced AI systems in healthcare applications, with a focus on research and development.
Salary
Base: $196,000.00 - $342,700.00; Bonus/Equity: Not specified; Benefits: Not specified
Skills & Requirements
Must-have
AI safety principles
foundation and predictive AI models
generative AI
autonomous agentic systems
safety-by-design principles
AI safety research and development
AI safety in regulated environment
Nice-to-have
technical leadership
policy influence
external ambassador
sustained mentorship program
cross-industry consortia
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
Demonstrated ability to reason about system-level behavior, failure modes, and risk
Excellent coding and software development capabilities
Experience working in highly interdisciplinary and matrixed environments