About the Role
We're looking for a Data Scientist to own the quality, reliability, and trustworthiness of our clinical AI outputs. You'll build the systems that ensure our AI "knows what it doesn't know" — developing evaluation frameworks, calibrated confidence scoring, and automated quality assurance that physicians can actually trust.
What You'll Do
• Design and implement automated evaluation pipelines that assess AI output quality, accuracy, and safety at scale
• Develop uncertainty quantification systems where confidence scores meaningfully correlate with accuracy
• Build comprehensive evaluation frameworks combining automated assessment with clinician-validated test cases
• Implement feedback loops that continuously improve model outputs based on validation signals
• Establish scalable quality gates that catch errors before they reach end users
• Contribute to model alignment and fine-tuning efforts
Qualifications
Required
• Strong foundation in deep learning frameworks (PyTorch) and LLM architectures
• Experience with model evaluation, benchmarking, and quality metrics
• Proficiency in Python and modern ML development tools
• Strong statistical foundations
• Ability to read, implement, and extend research papers
• Excellent communication skills
Preferred
• Master's degree in Computer Science, Machine Learning, Statistics, or related quantitative field (PhD preferred)
• Publications in top ML/AI venues (NeurIPS, ICML, ICLR, ACL)
• Experience with RLHF, DPO, or preference optimization techniques
• Background in healthcare AI or regulated industries
• Experience building evaluation systems for production LLM applications