AIML - Data Scientist, Responsible AI, Product Insights

Apple's Responsible AI Team is the steward of product safety for Apple Intelligence. We set safety policy and partner with feature engineering teams to ensure every generative AI experience Apple ships reflects our values. Our work spans three pillars: human and automated red teaming; pre-ship safety evaluation and post-ship monitoring; and the design of safety mitigations — overrides, guardrail models, and base model safety alignment. This role brings the data science lens to all three. You'll uncover and characterize safety weaknesses for red teaming, ground evaluations in real-world usage signal, and measure the in-production performance of the mitigations we deploy. The is a role for someone who loves the full arc of applied data science: scientific investigation, careful interpretation, cross-functional collaboration, and crisp communication of what the numbers actually mean. Minimum Qualifications MS, or PhD in Computer Science, Machine Learning, Statistics, or a related field; or equivalent experience. Strong foundation in data science, analytics, and machine learning, including statistical analysis, A/B testing, and the full lifecycle of designing, running, and interpreting experiments. Strong programming skills in Python and at least one data-querying language (SQL, Spark, or similar). Comfort with AI-assisted development tools such as Claude Code, Codex, or Copilot. Track record of framing research and business questions, and answering them with the right statistical tools applied to messy real-world data. Excellent technical communication skills, with a proven ability to explain hard ideas (especially causal ones) to audiences ranging from fellow data scientists to engineers to executive partners. Willingness to work with highly sensitive material, including exposure to offensive and controversial content. Preferred Qualifications Experience working in the Responsible AI space. Experience working with usage data from AI-powered products, and a current view on how these systems commonly fail. A record of scientific research and publication. Genuine curiosity about fairness and bias in generative AI, and a drive to make the technology more equitable.