Senior AI-Driven Search Systems Engineer

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

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.