rippling
Applied Scientist, AI Risk
Armilla AI
Toronto, CAN
Full-time
Mid
Onsite
Discovered 3 weeks ago
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The Role
We're seeking an exceptional Applied Scientist who bridges the worlds of deep AI research and practical, production-grade applications. As our Applied Scientist, AI Risk, you'll be instrumental in both advancing our understanding of AI systems and translating that knowledge into robust risk assessment and evaluation frameworks. This isn't just about building models—it's about understanding their failure modes, vulnerabilities, and real-world reliability. You'll develop AI systems that evaluate other AI systems, creating the next generation of automated risk assessment tools. You'll be shaping how the insurance industry evaluates and prices AI risk.
Role Responsibilities
- Conduct deep technical research into AI model behavior, failure modes, and edge cases, with a focus on practical risk assessment as well as deep research.
- Design and develop AI systems—including specialized models, agents, and automated evaluation pipelines—that assess the safety, reliability, and risk profiles of other AI systems.
- Build production-grade tooling and platforms for automated AI risk assessment, model testing, and continuous monitoring.
- Evaluate diverse AI systems—from traditional ML models to Large Language Models and multimodal systems—identifying insurable risks, vulnerabilities, and potential failure scenarios.
- Develop novel evaluation methodologies and metrics that capture AI-specific risks such as adversarial vulnerabilities, distribution shift, hallucinations, and behavioral misalignment.
- Collaborate closely with our underwriting and actuarial teams to translate technical findings into actionable risk insights and pricing signals.
- Stay at the forefront of AI research and safety literature, rapidly prototyping and validating new risk assessment techniques.
- Write clean, maintainable, and well-tested code that scales from research prototypes to production systems.
- Communicate complex technical concepts clearly to diverse stakeholders, from engineers to underwriters to executive leadership.
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