We build frontier foundation models that power intelligent experiences at Apple. Our team works across the full training lifecycle: including pre-training foundation models, and developing mid-training approaches that bridge general capability and task-specific performance. What makes our work distinct is that we're engineering models specifically for Apple silicon and optimized for experiences that are private, personal, and deeply integrated into the OS. We're solving frontier problems in reward modeling to resist reward hacking, handling sparse and delayed rewards in agentic settings, and aligning models reliably across the spectrum from open-ended creative tasks to precise, action-taking workflows. If you're drawn to hard problems where the research and the product are inseparable, this is the team.

Benchmark Design & Development: Design and implement evaluation benchmarks, metrics, and test suites that rigorously measure model capabilities across reasoning, knowledge, code, and agentic workflows.

Product-Aligned Evaluation: Develop evaluation methods that capture how models behave in real product settings, and validate that evaluation metrics predict user-perceived quality and product outcomes.

Evaluation Methodology Research & Tooling: Research and apply state-of-the-art evaluation techniques — including scoring frameworks, model-based judging, and contamination-resistant benchmark design. Build reusable tools, scorer libraries, and analysis frameworks that scale across the team's benchmark portfolio.

Experimental Analysis: Design and execute rigorous experiments comparing model capabilities, engage with third-party vendors on benchmarking, and perform detailed gap analysis to guide model development priorities.

Cross-Team Collaboration: Work closely with model training, training data, and product teams to ensure evaluation insights inform training strategies, data decisions, and product quality improvements.