ML Engineering Program Manager (EPM), Apple Ads

At Apple we work every day to create products that enrich peoples 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 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.

Own planning and execution of complex programs spanning ML frameworks platforms and algorithmic a strong understanding of system architecture and cross-team dependencies to anticipate risks and downstream with engineers researchers and technical leaders to translate strategic priorities into executable alignment on sequencing technical tradeoffs dependencies and launch readiness across engineering and product technical decisions program risks progress and outcomes clearly to technical teams and senior planning and execution practices including thoughtful use of AI and automation to increase team effectiveness.

5 years leading complex technical programs related to AI/ML understanding of ML systems software architecture and technical dependencies across large-scale record of driving engineering roadmaps and execution across multiple teams and technical to engage credibly with engineers and technical leaders ask effective technical questions and identify risks beyond individual written and verbal communication skills with technical teams and senior ability to learn complex technical domains quickly and operate independently in ambiguity.

Knowledge of ML workflows frameworks platforms prediction systems or algorithmic working with researchers or applied research teams to translate exploratory work into engineering with distributed systems low-latency architectures cloud platforms or other large-scale in advertising ranking recommendations experimentation or performance marketing use of AI or automation to improve engineering or program-management of influencing technical direction and challenging assumptions across teams without direct authority.