Principal Machine Learning Engineer - ML Innovation

We are seeking an exceptional Principal Machine Learning (ML) Engineer/Researcher to join our premier ML innovation team at Apple. As a key member of this team, you will drive the development and shipping of cutting-edge ML technologies that improve the lives of billions of Apple customers. The successful candidate will lead the design, implementation, and testing of innovative ML algorithms, methods, and systems for personalization, memory, diagnostics, representation and reasoning. This role requires a deep understanding of ML fundamentals, strong leadership skills, and the ability to collaborate with cross-functional teams.

Description

This is a technical lead position, working with both junior and senior ML engineers/researchers, and product engineers, in a cross functional settings. You core responsibilities will be: (Hands on) Lead or co-lead the design, implementation, and testing, of algorithms, methods, and systems for ML based personalization, memory, diagnostics, and reasoning. It entails working with multi-functional engineering teams, defining dependencies with other teams, defining API's and developing technical agreements. You will be expected to stay up to date up and monitor the latest developments in ML and explore their possible application to Apple products and services.

Preferred Qualifications

Proven leadership in shipping/designing ML based products.

Experience with cross-functional projects, including de-risking and scoping.

Strong understanding of end to end systems.

Minimum Qualifications

Strong ML expertise and deep understanding of the ML fundamentals including mathematics, architectures, representation and reasoning, generation/diffusion processes, and diagnostics

Strong Computer Science algorithmic and theory knowledge, and strong statistical fundamentals.

7 Years experience on training, evaluating, and characterizing ML models and end to end systems

5 Years of experience in at least one domain of application for ML: NLP, Computer Vision, reinforcement learning. A deep understanding of the applications, challenges, and opportunities in these domains.