Senior Search Engineer - Services Special Projects

Apple; United States; On-site; USD 180,000 - 280,000; Full time

Job summary

Apple is seeking a highly experienced Search Systems Engineer to design, develop, and optimize large-scale search systems integrating Generative AI and information retrieval. You will work with AI/ML scientists and engineers to craft intelligent systems that personalize user experiences.

The role focuses on ranking, retrieval, and multi-stage reranking using BM25 and vector embeddings, with production-grade deployment and A/B testing frameworks.

Qualifications

• 10+ years of experience in ML, Data Science, or Software Eng roles with a focus on search infra 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 (e.g. transformers, cross encoder models, graph neural networks, learned sparse representations).

Responsibilities

• Design, build, and maintain large-scale, low-latency, high-performance search systems.
• Develop and optimize ranking, relevance, and retrieval through ML/AI models.
• Merge traditional keyword search with vector-based semantic search.
• Develop ML models and multi-stage reranking to optimize precision and recall.

Skills

Go

Java

C++

Python

Machine Learning

Data Science

Search Infrastructure

Information Retrieval

NLP Pipelines

ML Frameworks

Education

Bachelor's or Master's in CS/ML/Statistics

PhD preferred

Tools

FAISS

OpenSearch

Elasticsearch

PyTorch

XGBoost

Spark

Flink

Docker

Kubernetes

TensorRT

ONNX Runtime

Job description

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.

Design, build, and maintain large-scale, low-latency, high-performance search systems that can scale.

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.

Develop sophisticated NLP pipelines for intent classification, entity extraction, semantic parsing, and query expansion.

Merge traditional keyword search (BM25) with vector-based semantic search using embedding models and vector databases.

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.

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 the latest research and innovations in search and information retrieval technologies, translating them into scalable production systems.

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 (e.g., transformers, cross encoder models, graph neural networks, learned sparse representations).

Experience with to multi-objective optimization in search systems (e.g., relevance, diversity, freshness, 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, XGBoost.

Familiarity with cloud environments (including AWS) and containerization (Docker, Kubernetes)

Extensive experience working with data processing pipelines including Spark, 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 with tuning of models (including quantization) for performance and quality optimization

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.

Experience with graph databases such as TigerGraph

Experience with data and model versioning tools and practices (e.g., DVC, MLflow, Weights & Biases)

Deep Experience with KV Stores including SSTables and Cassandra

Experience with tuning KV-cache and batching for low-latency, high-throughput real-time inference.

Deep production level experience with inference runtimes/compilers (ONNX Runtime, TensorRT/TensorRT-LLM), and serving frameworks (vLLM, SGLang or Triton, TorchServe ) .