Machine Learning Manager

Summary

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 Retail Machine Learning Team, we are responsible for applied machine learning solutions across on Apple’s Online and Retail stores. Our main goal is to deliver a magical, personal 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 offer such an extraordinary experience, 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 Senior Machine Learning Engineering leader. You 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 experience! This role spans central areas of our Apple Online Store and our Retail 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, financial fraud and abuse detection. As a leader of the fast-paced team, you will have the outstanding and great opportunity to be part of a new projects and craft upcoming products that will delight and encourage millions of Appleʼs customers every day.

Minimum Qualifications

9+ years related experience in enterprise applications with 5+ years of managing large engineering teams You can actively participate in investigations into realtime and offline analysis, and draw conclusions from data, recommend actions. Excellent leadership skills with a demonstrated ability to build and manage 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

Description

To be successful, you should have lead a team of Engineers and Technical Program Managers working with global team across multiple timezones and be positioned as the senior leader in region supporting the growth of the organization.. You 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

Attract, hire, and inspire a diverse world-class engineering and program management team, fostering a culture of innovation, collaboration, and excellence Develop the leadership strategy within region and drive the organizational relationships with our partners Enable the team members 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 identify opportunities for improvement and innovation and effectively communicate with stakeholders, providing regular updates on project status, progress, and any potential risks or challenges.