Sr ML Engineering Manager, Search - Services Special Projects

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.

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.