Machine Learning Engineering Manager

Summary

Posted: Nov 8, 2024

Role Number:200578135

Imagine what you could do here! The people here at Apple don’t just create products — they build the kind of wonder that’s revolutionized entire industries. It’s the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. At Apple, inclusion is a shared responsibility, and we work together to foster a culture where everyone belongs and is inspired to do their best work. Here on the Apple Store Online team, we are responsible for Apple’s largest store. Our main goal is to deliver a magical, personal digital experience where customers can shop, buy and learn everything Apple, wherever they are. Each customer should feel like they are our only customer and our job is to set the bar for the experience they receive. To run such an extraordinary store, it takes extraordinary people, and we are looking for someone to help us do extraordinary things. We are looking for a passionate, highly motivated, and hands-on applied Machine Learning Engineering leader. This role will lead the way on our Online Retail Decision Automation team by researching and developing the next generation of algorithms used to drive the Apple Online experience! The role spans central areas of our Apple Online Store including developing models for product search, recommendation systems (e.g. ranking, page generation), personalization (e.g. evidence, messaging, marketing), Generative AI and optimizing Apple-wide systems & infrastructure. As a leader of the fast-paced team, you will have the outstanding opportunity to be part of new projects and craft upcoming products that will delight and encourage millions of Appleʼs customers every day.

Description

To be successful, candidates need a strong machine learning background, proven software development skills, a love of learning, and to collaborate well in multi-disciplinary teams. You will need to exhibit strong communication and leadership skills, an ability to set priorities, and an execution focus in a multifaceted environment. RESPONSIBILITIES INCLUDE: – Attract, hire, and inspire a diverse world-class engineering team, fostering a culture of innovation, collaboration, and excellence. – Enable the engineers in the team to grow their careers by providing the right opportunities along with clear and timely feedback. – Lead team of machine learning engineers and collaborate with x-functional teams overseeing the entire development lifecycle from design, implementation to operationalize robust, scalable and efficient machine learning solutions. – Stay up-to-date with emerging technologies, industry trends, and advancements in ML practices to find opportunities for improvement and innovation and effectively communicate with partners, providing regular updates on project status, progress, and any potential risks or challenges.

Minimum Qualifications

• 5+ years related experience in enterprise applications with 2+ years of managing large engineering teams.
• Ability to actively participate in investigations into realtime and offline analysis, draw conclusions from data, and recommend actions.
• Excellent leadership skills with a demonstrated ability to build and lead high-performing engineering teams.
• Experience enabling large-scale, high throughput and distributed customer facing applications.

Preferred Qualifications

• Ph.D. or Masters in a quantitative field, such as Computer Science, Applied Mathematics, or Statistics, or equivalent professional experience.
• Experience in Recommender Systems, Personalization, Search, Computational Advertising or Natural Language Processing, including RAG based Generative AI and transformer architecture.
• Experience with building data processing pipelines, large scale machine learning systems, and big data technologies (eg: Spark, SQL, Snowflake/Hadoop, etc).
• Proficiency in one or more object-oriented programming languages such as Python, Java, C++ and experience building highly scalable distributed systems.