Search Engineer - Services Special Projects

Cupertino, California, United States Software and Services

Our team is building a massive, real-time search experience from the ground up — one that will reach users at Apple scale. It's search at the intersection of Generative AI and Information Retrieval, and it's a rare opportunity to shape a product that millions will rely on.

We are seeking a highly experienced and innovative Search Systems Engineer to help design, develop, and optimize large-scale search systems.

This role is ideal for a technically deep individual who has a strong product sense and enjoys solving real-world problems using modern AI models and scalable systems. We are a passionate team of hardworking engineers and scientists, and we are looking for a strong Search engineer to join us. You will work closely with AI/ML Scientists and engineers at the intersection of Generative AI and Information Retrieval, crafting intelligent systems that personalize user experiences.

• Search Architecture:
  Design, build, and maintain large-scale, low-latency, high-performance search systems that can scale.
• Build and optimize search and retrieval systems:
  Develop and optimize ranking, relevance, and retrieval through ML/AI models and merging traditional keyword search with vector-based semantic search using embedding models and vector databases.
• Query Understanding:
  Develop sophisticated NLP pipelines for intent classification, entity extraction, semantic parsing, and query expansion.
• Relevance & Ranking:
  Design and Implement machine learning models (e.g. Learning to Rank, Cross Encoder based models) and multi-stage reranking algorithms to optimize search precision and recall.
• Evaluation & Tuning:
  Build offline and online evaluation metrics, A/B testing frameworks, and continuous improvement strategies for search quality
• Collaborate cross-functionally:
  Partner with Research Scientists, Product, Data Engineering, MLOps, Search Infrastructure teams, and UX to align search features with business and user goals.
• Advance search research:
  Stay current with the latest research and innovations in search and information retrieval technologies, translating them into scalable production systems.

• Bachelor's degree in Computer Science, Machine Learning, Statistics, or a related field
• 8+ years of experience in Machine Learning, Data Science, or Software Engineering roles with a significant focus on search infrastructure and information retrieval.
• Validated experience building and deploying large-scale search systems in production.
• Strong proficiency in C++, Go, Python or Java
• Deep familiarity with ML frameworks (Tensor Flow, PyTorch, XGBoost, etc.).
• Solid understanding of ML system design, model lifecycle, and experimentation pipelines.
• Extensive experience working with large datasets, data processing pipelines (e.g., Spark, Flink), and scalable architectures.
• Deep understanding of information retrieval, ranking algorithms, and user modeling techniques.
• Experience with real-time systems, user feedback loops, and model retraining pipelines.
• Vector Infrastructure:
  Hands-on experience with vector databases such as Milvus, Qdrant, Pinecone, or FAISS.
• Working knowledge of cloud environments (AWS or GCP) and containerization (Docker, Kubernetes)
• Experience building streaming platforms such as Apache Kafka or comparable message brokers
• Experience with search infrastructure such as Open Search, Elasticsearch, or similar search-based stacks
• Excellent communication skills and a collaborative mindset

• Master's Degree;
  PhD Preferred
• Published work or patents in the domain of search systems, information retrieval, or related ML fields.
• Strong foundation in deep learning architectures for search and retrieval (e.g., transformers, graph neural networks, learned sparse representations).
• Exposure to multi-objective optimization in search systems (e.g., relevance, diversity, freshness, fairness).
• Familiarity with MLOps tools and cloud platforms (AWS/GCP, MLflow, etc.)
• Experience with graph databases such as Tiger Graph
• Experience with data and model versioning tools and practices (e.g., DVC, MLflow, Weights & Biases)

At…