Staff AI Scientist

Staff AI Scientist (Applied Research & ML Engineering)

Join Fiddler AI to lead applied research and development for models and datasets powering the company’s Trust Service and guardrail classifiers.

Responsibilities

• Lead applied research and development for models and datasets for Fiddler’s Trust Service and guardrail classifiers.
• Design, train, and ship production classifiers for safety, security, and quality under strict latency and cost constraints.
• Drive the technical direction of generative insights (LLM/agent-powered analysis layer) that helps customers diagnose failures in their AI applications.
• Partner closely with engineering teams on evaluation and experimentation infrastructure.
• Mentor AI Scientists and represent the company externally via publications and talks.
• Work with backend/platform engineers on real-time inference, monitoring, and rollouts.

Requirements

• Strong communication skills; can explain research tradeoffs to engineers, PMs, and customers.
• 7+ years of applied AI experience with a track record of shipping models to production.
• Proficiency in Python and the modern ML stack (PyTorch, Hugging Face, training/serving frameworks).
• Experience with LLM or agentic evals and guardrailing.
• Deep expertise training/fine-tuning classifier models (e.g., BERT-family, ModernBERT) and LLM-as-classifier approaches.
• Hands-on dataset development: sourcing, labeling, synthetic generation, adversarial augmentation, and quality control.
• Experience with LLMs and agentic systems: prompting, fine-tuning, and evaluation.
• Ability to work with enterprise/regulated industries (finance, healthcare, government).
• Must be able to work from the Palo Alto office 2–3 days/week.

Nice to Haves

• Background in AI safety, red-teaming, or adversarial ML.
• Experience with synthetic data generation pipelines at scale.
• Experience with RL/RLHF/RLAIF or preference-based fine-tuning.
• Published research at top ML/NLP venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP).
• M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, Physics, or a related quantitative field.