The Health Sensing team at Apple is looking to develop and validate evaluation methodologies for Generative AI systems in health and wellbeing applications. The goal is to ensure the quality and trustworthiness of AI features by creating scalable evaluation pipelines that combine human insight with automated validation.
Requirements
- Proficiency in Python and ability to write clean, performant code and collaborate using standard software development practices
- Experience in building data and inference pipelines to process large scale datasets
- Strong statistical analysis skills and experience validating data quality and model performance
- Experience with applied LLM development, prompt engineering, chain of thought, etc.
- Experience with LLM-based evaluation systems and synthetic data generation techniques, and evaluating and improving such systems
- Experience in rigorous, evidence-based approaches to test development, e.g. quantitative and qualitative test design, reliability and validity analysis
Responsibilities
- Design and implement evaluation frameworks for measuring model performance, including human annotation protocols, quality control mechanisms, statistical reliability analysis, and LLM-based autograders to scale evaluation
- Apply statistical methods to extract meaningful signals from human-annotated datasets, derive actionable insights, and implement improvements to models and evaluation methodologies
- 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.
- 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
- Collaborate with engineers to build reliable end-to-end pipelines for long-term projects
- Work cross-functionally to apply algorithms to real-world applications with designers, clinical experts, and engineering teams across Hardware and Software
- Independently run and analyze ML experiments for real improvements
Other
- Bachelors in Computer Science, Data Science, Statistics, or a related field; or equivalent experience
- MS and a minimum of 3 years of relevant industry experience or PhD in relevant fields
- Customer-focused mindset with experience or strong interest in building consumer digital health and wellness products
- Strong communication skills and ability to work cross-functionally with technical and non-technical stakeholders
