Machine Learning Engineer - News, Books, and Stocks Team

Apple • Cupertino, CA

7h

About The Position

Our team is seeking a high-energy and self-driven machine learning engineer who will play a central role in the delivery of scalable services. The team uses machine learning to tackle difficult and complicated problems in the news, books, and stocks domains, including text extraction, named entity recognition, duplicate detection, search, ranking, and much more! As a member of our dynamic group, you’ll have the rare and rewarding opportunity to craft upcoming products that will delight and encourage millions of Apple’s customers every day!

Requirements

• MS in Machine Learning, Computer Science, or related field, or equivalent industry experience.
• At least 2 years of experience shipping machine learning models in products.
• Strong programming skills in Python, Java, or a related language.
• Experience with one of the deep learning toolkits such as PyTorch, TensorFlow, or similar.
• Ability to communicate effectively and collaborate with partner teams.
• Commitment to encouraging an open and inclusive work environment.

Nice To Haves

• Ph.D. in Machine Learning, Computer Science, or related field.
• At least 5 years of experience shipping machine learning models in products.
• Experience with recommender systems.
• Experience with text-centric AI/ML (LLMs, document classification, search, etc.).
• Experience delivering high quality software at scale.
• Experience designing user-facing machine learning features with interdisciplinary partners.

Responsibilities

• Play a central role in the delivery of scalable services.
• Tackle difficult and complicated problems in the news, books, and stocks domains using machine learning.
• Develop features related to text extraction, named entity recognition, duplicate detection, search, ranking, and more.
• Craft upcoming products that will delight and encourage millions of Apple’s customers every day.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior