Apple-posted 2 months ago
Senior
Cupertino, CA
5,001-10,000 employees
Computer and Electronic Product Manufacturing

Apple's Special Projects team is seeking a Senior Engineering Program Manager (EPM) to lead our AI evaluation framework at the forefront of next-generation AI experiences. This is a highly visible role where you'll drive the strategic direction for how we measure and validate AI model performance across modalities. You will organize and lead teams to architect our evaluation methodology, translating ambiguous product requirements into concrete success metrics that determine if our models meet Apple's quality bar. Your work will directly influence product roadmaps and drive critical hill-climbing decisions that shape the AI experiences millions of users will interact with daily. This role requires mastery of both technical depth in ML evaluation and the ability to influence across teams at the executive level. You'll lead complex, high-stakes programs while mentoring teams and establishing best practices that will define Apple's approach to AI quality.

  • Partner with technical teams to define and drive the evaluation strategy for Apple's AI models, influencing product roadmaps and technology investments.
  • Partner with stakeholders to align evaluation frameworks with product vision and business objectives.
  • Develop end-to-end programs to measure and validate performance of state-of-the-art multi-modal models.
  • Drive consensus on subjective quality metrics, establishing clear success criteria for ambiguous use cases.
  • Orchestrate complex data collection initiatives, simulation infrastructure, and evaluation pipelines.
  • Deliver process improvements that increase efficiency across the broader organization.
  • Coordinate distributed teams across design, engineering, research, and operations.
  • Communicate complex technical concepts and program status directly to executive leadership.
  • Master's or PhD in Computer Science, Machine Learning, Statistics, or related quantitative field.
  • Deep hands-on experience with large language models (LLMs), vision-language models (VLMs), and multi-modal architectures.
  • Demonstrated mastery of ML concepts, evaluation methodologies, and the end-to-end model development lifecycle.
  • Experience with reinforcement learning from human feedback (RLHF) and preference optimization.
  • Expertise in statistical analysis, A/B testing, and experimental design at scale.
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