Machine Learning Research Engineer, ASE Search

Summary

The Apple Services Engineering (ASE) team is one of the most exciting examples of Apple's long-held passion for combining art and technology. People here create the experiences loved by users across App Store, Apple TV, Apple Music, Apple Podcasts, Apple Books, and Apple Fitness. The scale is massive, delivering content and entertainment in over 35 languages to more than 150 countries, while meeting Apple's high bar for quality and performance.

The team is responsible for building secure, robust, end-to-end solutions across server and client to solve challenging problems. Thanks to Apple's unique integration of hardware, software, and services, engineers here work with a single unified vision of deep commitment to strengthening Apple's core principles such as customer focus, privacy, and relentless innovation. Although services are a bigger part of Apple's business than ever before, these teams remain small, nimble, and cross-functional, offering an opportunity to work with passionate people, contribute ideas, and ship innovative software. Here, you'll do more than just join a team; you'll be making a positive impact on people's lives.

Description

The ASE Search team is a vital part of the Apple ecosystem, powering search for App Store, Apple Music, Apple TV, Podcasts, Books, Fitness+, iTunes, and more, across a wide set of platforms including iOS, macOS, tvOS, watchOS, visionOS, Safari, and third-party devices. Driven by a passion for the extraordinary rather than the easy, our team of problem solvers is dedicated to helping users discover media and content in exciting new ways, and we're looking for motivated engineers and researchers to join us on this journey.

As a Machine Learning Researcher/Engineer on the ASE Search team, you will help design and develop next-generation search and conversational discovery features for Apple's groundbreaking devices and platforms.

Responsibilities

• Contribute to building experiences that shape how people search and discover on Apple devices worldwide.
• Build and improve ML models and systems across surface areas such as retrieval, ranking, query understanding, and document understanding.
• Develop and iterate on ML models end-to-end, from data preparation and training through evaluation and production deployment, including backend components using languages such as Go, Java, Python, and Scala.
• Stay current with research in search, information retrieval, and generative AI, and apply new techniques to improve our systems.
• Collaborate with engineers, researchers, and product teams to understand requirements and deliver high-quality solutions.
• Design and run A/B experiments, and contribute to automated testing, monitoring, and alerting to measure and maintain the health of production systems.
• Present technical work to the team and participate in design and code reviews.

Minimum Qualifications

• MS in Computer Science or a related subject area.
• 2+ years of relevant industry experience in ML or data systems.
• Knowledge of generative AI systems, including Large Language Models, Transformers, and techniques such as RAG and fine-tuning.
• Experience with one or more ML frameworks such as PyTorch or TensorFlow; familiarity with distributed training or inference tooling (e.g., Ray, TensorRT, vLLM) is a plus.
• Familiarity with search, recommendation systems, conversational engines, or related domains.
• Strong communication skills and the ability to work effectively in a collaborative team environment.

Preferred Qualifications

• Ph.D. in Computer Science or a related subject area.
• 4+ years of relevant industry experience in ML or data systems.
• Experience building search or conversational capabilities such as query understanding, retrieval, ranking, indexing, autocomplete, or intent resolution.
• Familiarity with big data pipelines using Scala, Python, or Apache Spark.
• Exposure to distributed backend services, Kubernetes, cloud infrastructure, or container orchestration.
• Experience with GoLang or gRPC services.
• Familiarity with A/B experimentation and data-driven product development.