Proficiency in Python with the ability to write production-grade code (OOP, CI/CD, Git) Hands-on experience in failure analysis, evaluating LLMs and driving subsequent model improvements Experience building data pipelines, inference frameworks, and automated evaluation systems Strong communication skills to articulate complex technical concepts across technical and non-technical audiencesMS/PhD in Computer Science, Machine Learning, Statistics, or related field Experience evaluating LLMs or agentic systems (e.g., LLM-as-a-judge, RAG evaluation) Experience with synthetic data generation and prompt engineering Experience in parallel data processing (Spark, Kubernetes, Airflow) or privacy-preserving ML (Federated Learning) Background in AI Safety, model interpretability, or adversarial testing Interest in digital health and clinical rigor. Develop generalizable tools and metrics to discover biases and measure demographic equity across diverse populations Translate evaluation results into actionable engineering insights for GenAI researchers, algorithm leads, and clinical experts.