Staff Machine Learning Research Engineer, App Store Search

The Apple Services Engineering (ASE) team is one of the most exciting examples of Apples 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 delivering content and entertainment in over 35 languages to more than 150 countries while meeting Apples 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 Apples unique integration of hardware software and services engineers here work with a single unified vision of deep commitment to strengthening Apples core principles such as customer focus privacy and relentless innovation. Although services are a bigger part of Apples 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 youll do more than just join a team youll be creating positive impact in peoples lives.

The ASE Search team is a vital part of 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 and 3rd party devices. 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 extraordinary and motivated machine learning researchers and engineers to join us in our journey. As a Senior/Staff Machine Learning Research on the ASE Search team you will lead the design and development of next-generation search and conversational discovery features for Apples ground breaking devices and platforms.

Build next-gen experiences influencing the way people search u0026 discover on Apple devices innovation by staying at the forefront of research and applying it to complex problems with rigor and principled foundational systems across multiple surface areas retrieval ranking indexing query and document understanding and agentic/RAG-based and drive the technical roadmap for search ML capabilities aligning short-term execution with long-term strategic with engineers researchers product and platform teams to identify high-impact opportunities and deliver robust the ML lifecycle end-to-end from formulating objectives and defining data and training strategies through production components in large-scale distributed backend systems using high-performance programming languages such as Go Java Python and key technical and novel research work in public A/B experiments with well-defined KPIs to objectively measure testing monitoring and alerting solutions to maintain high operational SLAs for production engineers and raise the bar on ML best practices code quality and system design.

3 years of relevant industry experience building large-scale ML u0026 data systemsnFamiliarity with search or recommendation systems conversational engines or related domainsnStrong knowledge of generative AI systems including Large Language Models Transformers Reinforcement Learning RAG and agentic patterns such as ReAct Chain-of-Thought Tool Use and Multi-Agent orchestrationnExperience with one or more distributed ML training frameworks such as PyTorch TensorFlow Ray or JAX and inference engines like TensorRT or vLLMnTechnical leader with exceptional communication skills and a track record of solving complex ambiguous problems in a highly collaborative environment

MS or Ph.D. in Computer Science or related subject areanProven ability to build u0026 scale Search u0026 Conversational systems applying 7 years of hand-on experience across the full product stack including query understanding semantic retrieval multi-stage ranking indexing intent classification and context-aware expertise in Search u0026 Conversational systems bringing in 7 years of hands-on experience building capabilities such as query understanding retrieval ranking indexing autocomplete intent resolution and context-aware generation across multiple in developing robust big data pipelines in Scala or Python using distributed processing frameworks like Apache with scalable reliable distributed backend services including Kubernetes cloud infrastructure and container orchestrationnFamiliarity with A/B experimentation and data-driven product development