Applied Machine Learning Engineer, Services Engineering

Summary

The Apple Services Engineering (ASE) team is behind some of the best experiences on the planet. These are the people who develop engineering solutions to power seamless experiences across Apple Services such as Apple Music, Apple TV, Podcasts, Fitness+, Books and the App Store. And they do it at scale, delivering a best-in-class experience worldwide. While ASE is rapidly emerging to be a big part of Apple’s business, our teams remain small and nimble.

Thanks to Apple’s unique integration of hardware, software, and services, we partner cross-functionally to get behind a single unified vision. That vision always includes a deep commitment to strengthening Apple’s privacy policy, one of Apple’s core values.

Summary

The Apple Services Engineering (ASE) team is behind some of the best experiences on the planet. These are the people who develop engineering solutions to power seamless experiences across Apple Services such as Apple Music, Apple TV, Podcasts, Fitness+, Books and the App Store. And they do it at scale, delivering a best-in-class experience worldwide. While ASE is rapidly emerging to be a big part of Apple’s business, our teams remain small and nimble.

Thanks to Apple’s unique integration of hardware, software, and services, we partner cross-functionally to get behind a single unified vision. That vision always includes a deep commitment to strengthening Apple’s privacy policy, one of Apple’s core values.

We are now looking for an extraordinary Applied Machine Learning Engineer to join our team and develop innovative AIML solutions. We are in early days of what we have set out to do, so you have the opportunity to influence our journey and deliver significant, step function changes along the way. We’re looking for someone whose “people skills” complement their technical skills, someone who loves improving things as much as building things, and someone who is passionate about discovering new ways to accelerate the rate at which we can improve our products.

If this sounds like you, join our team!

Description

In this role, you will

• Engage in research and development to advance state-of-the-art AIML technology and adopt them to power Apple’s use-cases
• Develop time-series models that can be leveraged for forecasting, infrastructure capacity planning, anomaly detection, and other such usecases.
• Take end-to-end ownership of data pipelines and ML models from research stage all the way to scaling and production. Write production grade code that is robust, reusable, testable and maintainable, and engage in code review with peers.
• Collaborate with broader teams across Apple and influence engineering design decisions that ensure high quality of features and technologies shipped. Provide technical leadership to junior members of the team.

• Masters or PhD in Computer Science and Engineering, Machine Learning, Operations Research, or equivalent academic and/or professional experience.
• 5+ years of hands-on industry experience and a proven track record of shipping ML-powered products or features. Extensive experience with the ML development loop, with expertise in model architectures, data curation, training policies and evaluation.
• Proficiency in Python, SQL, PyTorch (and/or one of the deep learning frameworks such as JAX, Tensor Flow).
• Strong system design skills, coupled with a proven understanding of data structures and algorithms.
• Deep understanding of fundamental concepts in statistics, optimization, time series modeling, classical machine learning, and deep learning.
• Excellent communication (strong interpersonal, verbal and written skills), and ability to partner effectively with engineering, research, and product stakeholders.

• Experience with time-series forecasting, compute infrastructure capacity planning and efficiency.
• Experience working with, or developing, time-series foundation models.
• Experience with deploying production grade distributed data processing (e.g. PySpark) and ML pipelines, and developing large-scale ML infrastructure, such as services, frameworks or tooling.