Senior Search Engineer - Services Special Projects

Apple • Cupertino, CA

11d

About The Position

The Senior Search Engineer will help design, develop, and optimize large-scale search systems for a real-time search experience at Apple scale, operating at the intersection of Generative AI and Information Retrieval. The role combines technical depth and product sense to solve real-world problems with modern AI models and scalable systems. The engineer will collaborate with AI/ML scientists and engineers to create intelligent systems that personalize user experiences.

The position focuses on building high-performance, low-latency search systems and improving ranking, relevance, and retrieval. It involves combining traditional keyword search with vector-based semantic search, developing NLP pipelines, implementing machine learning models and multi-stage reranking, and establishing evaluation and experimentation practices to continuously improve search quality. The engineer will partner with research, product, data engineering, MLOps, search infrastructure, and UX teams to align search capabilities with business and user goals, and will translate advances in search and information retrieval research into scalable production systems.

Requirements

• Bachelor’s or master’s degree in Computer Science, Machine Learning, Statistics, or a related field.
• 10+ years of experience in Machine Learning, Data Science, or Software Engineering roles, with a significant focus on search infrastructure and information retrieval.
• Hands-on experience building and deploying large-scale search systems in production.
• Deep understanding of information retrieval, query understanding, query augmentation, and multi-stage ranking algorithms.
• Strong foundation in deep learning architectures for search and retrieval, such as transformers, cross-encoder models, graph neural networks, and learned sparse representations.
• Experience with multi-objective optimization in search systems, such as relevance, diversity, freshness, and fairness.
• Experience with real-time systems, user feedback loops, and model retraining pipelines.
• Strong proficiency in Go, Java, C++, and Python.
• Proven experience with ML frameworks, including PyTorch and XGBoost.
• Familiarity with cloud environments, including AWS, and containerization tools such as Docker and Kubernetes.
• Extensive experience working with data processing pipelines, including Spark and Flink.
• Hands-on experience with vector search, including FAISS.
• Familiarity with streaming platforms, including Apache Kafka.
• Experience with search infrastructure, including OpenSearch and/or Elasticsearch.
• Hands-on experience deploying, serving, and optimizing LLMs, embeddings, and ML models directly in the production query/request path.
• Past successful deployments involving model tuning, including quantization, for performance and quality optimization.
• Excellent communication skills and a collaborative mindset.

Nice To Haves

• Master’s degree; PhD preferred.
• Published work or patents in search systems, information retrieval, or related ML fields.
• Experience with graph databases such as TigerGraph.
• Experience with data and model versioning tools and practices, such as DVC, MLflow, and Weights & Biases.
• Deep experience with KV stores, including SSTables and Cassandra.
• Experience tuning KV-cache and batching for low-latency, high-throughput real-time inference.
• Deep production-level experience with inference runtimes or compilers such as ONNX Runtime, TensorRT/TensorRT-LLM, and serving frameworks such as vLLM, SGLang, Triton, or TorchServe.

Responsibilities

• Design, build, and maintain large-scale, low-latency, high-performance search systems that can scale.
• Develop and optimize ranking, relevance, and retrieval using ML/AI models.
• Combine traditional keyword search, such as BM25, with vector-based semantic search using embedding models and vector databases.
• Develop NLP pipelines for intent classification, entity extraction, semantic parsing, and query expansion.
• Design and implement machine learning models, including Learning to Rank and cross-encoder-based models, and multi-stage reranking algorithms to optimize search precision and recall.
• Build offline and online evaluation metrics, A/B testing frameworks, and continuous improvement strategies for search quality.
• Partner with Research Scientists, Product, Data Engineering, MLOps, Search Infrastructure teams, and UX to align search features with business and user goals.
• Stay current with research and innovations in search and information retrieval, and translate them into scalable production systems.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior