Data Scientist (AI Quality & Evaluation)

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