The Apple Services Engineering (ASE) team is looking to solve the business and technical problem of powering Search for the App Store, Apple TV, Apple Music, Apple Podcasts, and Apple Books on a massive scale, ensuring these systems are fast, reliable, and built for scale, while also driving the creation of new online and offline components using SOTA technologies and building ML models.
Requirements
- Expertise in large-scale search systems, ranking/retrieval architectures, personalization pipelines, or agentic system design.
- Experience building production ML systems or bootstrapping first version of ML models.
- Strong systems engineering fundamentals, APIs, protocols, distributed systems, data modeling, performance, and reliability.
- Experience building complex systems involving indexing, retrieval, serving, data pipelines, or ML-driven components.
- 5+ years leading engineering teams delivering large-scale distributed systems, backend infrastructure, or search platforms.
Responsibilities
- Own the architecture and technical direction for search, personalization, agentic systems, and internal tooling.
- Define and implement core system components, including protocols, data flow, indexing, serving, inference, and automation.
- Lead development of new systems, both online and offline, using state-of-the-art technologies, algorithms, and ML models.
- Build ML models when appropriate, or collaborate with the Relevance Intelligence team for deeper model development.
- Partner closely with product, infrastructure, and cross-functional teams to ensure system robustness, scalability, and long-term evolution.
- Deliver reliable, production-ready systems with clear execution focus, technical rigor, and user-centered design.
Other
- Develop and mentor a high-performing engineering team with strong fundamentals and a bias for impact.
- Ability to lead cross-functional alignment and balance long-term platform strategy with fast iteration.
- Effective communication and leadership skills; able to drive clarity, prioritize well, and develop engineering talent.
- MS or Ph.D. in Computer Science, Machine Learning, Information Retrieval, or a related field.
- Strong execution record across the full engineering lifecycle.
