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.