Staff Machine Learning Research Engineer - App Store 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, and Apple Books. The scale is massive 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 — you’ll be creating positive impact in people’s lives.

Minimum Qualifications

3+ years of relevant industry experience building large-scale ML & data systems Familiarity with search or recommendation systems, conversational engines, or related domains Strong 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 orchestration Experience with one or more distributed ML training frameworks such as PyTorch, TensorFlow, Ray, or JAX, and inference engines like TensorRT or vLLM Technical leader with exceptional communication skills and a track record of solving complex, ambiguous problems in a highly collaborative environment

Preferred Qualifications

MS or Ph.D. in Computer Science or related subject area Proven ability to build & scale Search & 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 generation. Deep expertise in Search & 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 domains. Proficient in developing robust big data pipelines in Scala or Python using distributed processing frameworks like Apache Spark. Familiarity with scalable, reliable distributed backend services including Kubernetes, cloud infrastructure, and container orchestration Familiarity with A/B experimentation and data-driven product development

Description

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 Apple's ground breaking devices and platforms.

Responsibilities

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