Sr Engineering Program Manager, Evaluation - Special Projects
Summary
Lead Apple’s AI evaluation framework, defining metrics and methodologies to validate next-gen AI models across modalities and shape product roadmaps.
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.
As the EPM for Evaluation, you will be responsible for :
Minimum Qualifications
- 7+ years of technical program management experience, with at least 3 years leading complex AI/ML programs.
- Proven track record of delivering complex programs by defining clear requirements and driving engineering teams to successful outcomes.
- Experience influencing and driving decisions at Director/VP level.
- Strong ability to navigate ambiguity and lead teams through uncertainty while maintaining program momentum.
- Excellence in executive communication - ability to distill complex technical information with the right balance of detail.
Preferred Qualifications
- 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.