Sr ML Engineering Manager, Search - Services Special Projects

Summary

We're building a massive, real-time search experience that sits at the intersection of Generative AI and Information Retrieval! We make sense of high-volume structured and multimodal data and complex behavioral signals which deliver results that feel instant and relevant while still being private.

Join our team as a ML Search Engineering Manager and take part in this rare opportunity to shape a user-facing product that millions of Apple customers rely on every day!

Description

We are looking for a Search Engineering Manager & Lead to serve as both the senior technical
authority and the people leader for our search team. You'll own the architecture and long-term technical roadmap for large-scale, low-latency search infrastructure, from query understanding and hybrid retrieval through ranking and evaluation, and you'll also build, grow, and lead the team of search engineers who bring that roadmap to life.

This is a hands-on leadership role with dual scope: you set the technical vision and personally shape the hardest retrieval and ranking decisions, and you also manage, mentor, and grow the engineers executing against it. Your leverage comes equally from what you design and from the team you build.

Responsibilities

• 1. Architecture & Design (Architect scope)
• Set technical direction: own the architecture and long-term technical roadmap for large-scale, low-latency search infrastructure, making build-vs-buy and platform tradeoffs that the team executes against.
• Lead query understanding and retrieval strategy: guide the evolution of search pipelines, including autocomplete, query suggestions, and core search, intent classification, entity extraction, semantic parsing, and query expansion, and hybrid retrieval approaches spanning real-time, vector-based, and natural language search.
• Drive ranking strategy: set direction for relevance and ranking approaches (Learning to Rank, cross-encoder rerankers, multi-stage pipelines), driving AI/ML-powered search quality improvements that deliver measurable relevance gains, and review designs before they ship.
• Own evaluation rigor: drive the offline evaluation frameworks and online A/B testing methodology the team uses to validate search quality improvements.
• Track the state of the art: stay current with search and IR research, and translate promising techniques into scalable, production-ready designs for the team to build.
• Treat privacy as an architectural constraint: apply data minimization and privacy-preserving techniques to any user behavioral signal used in ranking or retrieval
• Own safety and trust for generative search results: set the guardrails against hallucination and harmful or misleading AI-generated answers, partnering with Trust & Safety on red-teaming and safety evaluation.
• Lead the development of generative AI-powered search features, and invest in developer productivity and tooling that let the team ship search capabilities faster.
• 2. Technical Leadership & Implementation (Lead scope)
• Raise the technical bar: lead design and code reviews, and establish the engineering standards and best practices the team builds against.
• Represent the team technically: act as the primary technical voice in cross-functional design reviews with Research Scientists, Product, Data Engineering, MLOps, and Search Infrastructure teams.
• Unblock the hardest problems: stay hands-on enough to jump into the most ambiguous or highest-risk technical problems, such as scaling bottlenecks, ranking regressions, or novel retrieval techniques, rather than delegating them away
• Drive the team's execution against the technical roadmap, from design through production delivery, and communicate progress, trade-offs, and risks to senior leadership and partner orgs.
• 3. Team Leadership & Management (People scope)
• Partner with recruiting to attract, evaluate, and hire senior and staff search engineers, raising the technical bar with every hire.
• Manage a group of search engineers directly, owning their performance, career development, and technical growth, and mentor across levels on search and IR fundamentals, ranking, and retrieval systems.
• Allocate work against the roadmap, unblock execution, drive design reviews, and hold a high bar for engineering craft and operational excellence.
• Advocate for the investments the search platform needs, and communicate progress and risk to senior leadership and partner orgs.
• Cultivate a healthy engineering culture: high ownership, strong review practices, and a deep commitment to search quality and user trust.

Minimum Qualifications

• MS in Computer Science, Engineering, or a related technical field, or equivalent experience. PhD preferred.
• 12+ years of experience in Machine Learning, Data Science, or Software Engineering, with a significant focus on search infrastructure and information retrieval, including at least 5 years operating in a technical leadership or engineering management capacity
• Proven experience leading and managing engineers, including hiring, performance management, and technical mentorship of senior and staff ICs.
• Track record of leading the architecture of large-scale search systems from design through production.
• Deep understanding of information retrieval, ranking algorithms, and user modeling techniques.
• Experience designing offline evaluation frameworks and online A/B testing methodology to validate search relevance and ranking quality.
• Experience with vector databases (Milvus, Qdrant, Pinecone, or FAISS).
• Experience with search infrastructure such as OpenSearch, Elasticsearch, or similar stacks.
• Experience with cloud environments (AWS or GCP), containerization (Docker, Kubernetes), and streaming platforms (Kafka or comparable brokers).
• Excellent written and verbal communication, with the ability to align engineers, partner teams, and senior leadership around a shared technical direction.
• Strong proficiency in a systems language such as Go or C++, with working proficiency in Java or Python
• Deep familiarity with ML frameworks (TensorFlow, PyTorch, XGBoost, or similar) and ML system design, model lifecycle, and experimentation pipelines.
• Extensive experience with large datasets, data processing pipelines (Spark, Flink), and scalable architectures.
• Working knowledge of data privacy principles (e.g., data minimization, privacy-preserving techniques) and experience applying them to systems that use user behavioral signals.
• Experience implementing safety guardrails for generative AI outputs, including hallucination mitigation, harmful-content filtering, and red-teaming or adversarial evaluation practices.

Preferred Qualifications

• Published work or patents in search systems, information retrieval, or related ML fields.
• Strong foundation in deep learning architectures for search and retrieval (transformers, graph neural networks, learned sparse representations).
• Exposure to multi-objective optimization in search (relevance, diversity, freshness, fairness).
• Track record of scaling engineering teams and modernizing infrastructure with measurable cost and reliability improvements.