What are the responsibilities and job description for the ML Engineering Program Manager (EPM), Apple Ads position at Apple?
At Apple, we work every day to create products that enrich people’s lives. Our Apple Ads group makes it possible for people around the world to easily access informative and imaginative content on their devices while helping publishers and developers promote and monetize their work. Our platforms are highly performant, deployed at scale, and set new standards for enabling effective advertising while protecting user privacy.
The Apple Ads EPM team is looking for a senior ML-focused Engineering Program Manager to lead complex initiatives across our machine learning and algorithmic systems. You will partner closely with engineers, researchers, product managers, and technical leaders to drive strategic roadmaps, connect dependencies across teams, anticipate risks, and deliver scalable ML capabilities that support Apple Ads.
You will operate at the intersection of machine learning, engineering, and program execution, developing enough technical depth to understand how systems work end-to-end and how changes may impact teams across the organization. This role requires strong systems thinking, curiosity, and communication, with the ability to bring structure to ambiguous technical problems and influence teams toward shared outcomes.
Minimum Qualifications:
5 years leading complex technical programs related to AI/ML systems.
Strong understanding of ML systems, software architecture, and technical dependencies across large-scale platforms.
Track record of driving engineering roadmaps and execution across multiple teams and technical disciplines.
Ability to engage credibly with engineers and technical leaders, ask effective technical questions, and identify risks beyond individual workstreams.
Excellent written and verbal communication skills with technical teams and senior leadership.
Demonstrated ability to learn complex technical domains quickly and operate independently in ambiguity.
Preferred Qualifications:
Knowledge of ML workflows, frameworks, platforms, prediction systems, or algorithmic improvements.
Background working with researchers or applied research teams to translate exploratory work into engineering execution.
Familiarity with distributed systems, low-latency architectures, cloud platforms, or other large-scale systems.
Background in advertising, ranking, recommendations, experimentation, or performance marketing systems.
Demonstrated use of AI or automation to improve engineering or program-management workflows.
History of influencing technical direction and challenging assumptions across teams without direct authority.