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Fiddler AI is an AI Observability platform company founded by engineering and product leaders from Facebook, Pinterest, Twitter, Microsoft, and Samsung. The company helps organizations deploy trustworthy, transparent AI solutions by enabling monitoring, evaluation, security, and improvement of AI applications at scale.
As Staff AI Scientist, you will lead applied research and development for the models and datasets at the core of Fiddler's Trust Service and suite of guardrail classifiers and evaluators. Your work will directly impact how enterprise customers keep their LLM and agentic applications safe, accurate, and compliant in production.
Key responsibilities include:
- Lead applied R&D for production classifiers detecting safety, security, and quality issues (prompt injection, jailbreaks, PII, hallucination, faithfulness) under strict latency and cost constraints
- Design and develop synthetic and adversarial dataset pipelines with novel methods for generating, filtering, and validating data that exposes model failure modes
- Drive technical direction of generative insights—the LLM- and agent-powered analysis layer helping customers diagnose AI application failures
- Contribute to evaluation and experimentation infrastructure for measuring model quality, regression, and drift across evolving model populations
- Explore reinforcement learning and preference-based methods where they offer leverage over supervised baselines
- Collaborate with Backend and Platform engineers to move research prototypes from notebook to hardened, scaled, observable services
- Partner with Product, Solutions Engineering, and Customer Success to translate enterprise needs into research problems and research results into product features
- Mentor AI Scientists on the team, raise technical standards through code and design review, and represent Fiddler externally through publications, talks, or open-source contributions
You'll work in a hybrid capacity with a tight-knit, collaborative AI Science team that partners closely with leading enterprise AI organizations. The team emphasizes knowledge sharing, peer learning, and collective problem-solving.
