Engineering Program Manager, Annotation & Evaluation

Summary

Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Do you love taking on challenges that create a positive impact? Are you passionate about empowering many ground-breaking intelligent experiences to be made? The annotation team enables internal product teams, data scientists and ML engineers through technologies that combine the latest in ML innovation and deep integration with Apple infrastructure. We’re looking for a strong Engineering Program Manager with a proven track record of building and maintaining complex software platforms, tools and processes that deliver new end-to-end experiences that delight customers.

Description

You will partner with engineering teams to deliver the technology that powers Apple’s ML feature development, all while contributing to a positive work culture. Apple’s annotation teams build the tools that allow machine learning engineers and researchers across Apple to create datasets that enable the amazing intelligent experiences on our current and future Apple devices. This role requires technical depth in complex system architecture with strong program management expertise to drive data generation that enables the comprehensive assessment of model capabilities, safety, helpfulness, and user experience quality.

Minimum Qualifications

Excellent project management skills including project structuring and managing multiple work streams interdependently Solid understanding of technical system architecture, systems design tradeoffs, and development and release pipelines for complex software Self-motivated and dedicated with demonstrated creative and critical thinking capabilities Ability to communicate abstract ideas clearly and independently manage complex project objectives

Preferred Qualifications

Experience in orchestrating the delivery of multiple significant software products requiring large organisations (100+) to execute Solid knowledge of technical system architecture and systems design tradeoffs, and thorough understanding of testing, build and release processes for complex software Solid understanding of the machine learning life cycle, data annotation and human-in-the-loop model evaluation Master's degree in Statistics, Machine Learning, Computer Science, other Quantitative Sciences, or related field and equivalent experience