In this role, you will be at the forefront of developing, evaluating and improving generative models for real-world health/wellbeing applications on their objective quality and alignment with human intent and perception, such as truthfulness, adaptability, and model generalizability. You will work on data and evaluation pipeline of both human and synthetic data for model evaluation, leverage ML technologies such as reinforcement learning with human feedback and adversarial models. - Work across the entire ML development cycle, such as developing and managing data from various endpoints, managing ML training jobs with large datasets, and building efficient and scalable model evaluation pipelines- Analyze model behavior, identify weaknesses, and drive design decisions with failure analysis. Examples include, but not limited to: model experimentation, adversarial testing, creating insight/interpretability tools to understand and predict failure modes, designing and improving human evaluation/annotation methods to accurately measure model performance- Ability to independently run and analyze ML experiments for real improvements