681 jobs for "AI Data & Infrastructure Engineer"

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.

Description

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.

","responsibilities":"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.

Preferred Qualifications

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 ) .

Minimum Qualifications

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

Pay & Benefits

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $184,700 and $324,800, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

About Apple

Working at Apple means doing more than you ever thought possible and having more impact than you ever imagined.

Size: 10,000+ employees
Industry: Technology

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