Machine Learning Research Engineer, App Store Search

Apple
Apple

Software Engineering, Data Science

Seattle, WA, USA

USD 142,300-263,300 / year + Equity

Posted on Jul 21, 2026
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, and Apple Books. The scale is massive â delivering content and entertainment in over 35 languages to more than 150 countries, while meeting Apple's high bar on 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 â you'll be creating positive impact in people's lives.
The ASE Search team is a vital part of the Apple ecosystem, powering search for App Store, Apple Music, Apple TV, Podcasts, Books, iTunes and more, on a wide set of platforms such as iOS, macOS, tvOS, watchOS, Safari, 3rd party devices, and Windows. Driven by 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. We are looking for motivated machine learning engineers and researchers to join us in our journey. As an ML Research Engineer on the ASE Search team, you will contribute to the design and development of next-generation search and conversational discovery features for Apple's groundbreaking devices and platforms.
  • Contribute to building experiences that shape how people search & discover on Apple devices worldwide.
  • Stay current with research in search, information retrieval, and generative AI, and apply new techniques to improve our systems.
  • Build and improve ML models and systems across surface areas such as retrieval, ranking, query understanding, and document understanding.
  • Collaborate with engineers, researchers, and product teams to understand requirements and deliver high-quality solutions.
  • Develop and iterate on ML models — from data preparation and training through evaluation and production deployment.
  • Build components in large-scale distributed backend systems using high-performance programming languages such as Go, Java, Python, and Scala.
  • Design and run A/B experiments to objectively measure the impact of changes.
  • Write automated tests and contribute to monitoring and alerting for production systems.
  • Present technical work to the team and participate in design and code reviews.
  • MS or Ph.D. in Computer Science or 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 search, recommendation systems, conversational engines, or related domains
  • Strong communication skills and ability to work effectively in a collaborative team environment
  • MS or Ph.D. in Computer Science or related subject area
  • 4+ years of relevant industry experience in ML or data systems
  • Experience in building search or conversational capabilities like query understanding, retrieval, ranking, or indexing
  • Familiarity with big data pipelines using Scala, Python, or Apache Spark
  • Exposure to distributed backend services, Kubernetes, or cloud infrastructure
  • Experience with GoLang or gRPC services
  • Familiarity with A/B experimentation and data-driven product development