Imagine what you could do here. At Apple, great ideas quickly become extraordinary products, services, and customer experiences. Bring passion and dedication to your work, and there’s no telling what you could accomplish.
Do you want to make Siri and Apple products smarter for our users? The Information Intelligence teams are building groundbreaking technology for algorithmic search, machine learning, natural language processing, and artificial intelligence. The features we build are redefining how hundreds of millions of people use their computers and mobile devices to search and find what they are looking for.
Within this organization, our group focuses on web-scale extraction and enrichment, transforming raw web content into structured, high-quality knowledge that powers Apple’s intelligent experiences. We design scalable extraction algorithms, develop advanced web data deduplication techniques, and apply machine learning to process trillions of records and petabytes of data. We also build and maintain Apple’s Knowledge Graph, integrating diverse data sources into a unified representation of the world knowledge.
We’re looking for a Machine Learning Engineer with deep expertise in large-scale data and ML infrastructure. You will build and optimize pipelines that extract, process, and serve data artifacts while advancing the ML frameworks and tooling that underpin Apple’s knowledge and search systems.
Description
Join a dynamic team within Apple’s Information Intelligence Infrastructure organization that designs, builds, and operates large-scale systems powering search and AI experiences for billions of users. We develop distributed, data-intensive infrastructure that processes trillions of records and petabytes of web data, enabling large-scale extraction, enrichment, and knowledge graph construction across diverse content such as HTML, PDF, and other unstructured formats.
Minimum Qualifications
Bachelor’s degree or higher in Computer Science or related technical field.
Experience with Golang, Java or Scala.
Background in computer science: algorithms, data structures, and distributed systems
Experience working in a cloud-native environment such as AWS
Experience working with large-scale data processing pipelines (Spark, Cassandra, etc.)
Experience with micro-service architecture in a containerized environment (Docker, Kubernetes, etc.)
Experience with machine learning workflows, including feature engineering, training, evaluation, deployment and serving.
Preferred Qualifications
Experience with training and fine-tuning large language models
Experience with optimizing ML training and serving performance, including GPU tuning, batch size optimization, and multi-node scheduling
Familiar with Nvidia TensorRT-LLM, vLLLM, Nvidia Triton Server etc.
Excellent interpersonal skills, able to work independently as well as in a team
Responsibilities
Tackle web-scale extraction and enrichment challenges, transforming raw web data into structured knowledge that powers Siri, Spotlight, Safari and other Apple experiences.
Design and optimize pipelines that leverage large language models (LLMs), advanced NLP, and entity linking frameworks to identify, deduplicate, and contextualize information from the open web.
Design and evolve the infrastructure that powers large-scale extraction and enrichment pipelines, enabling Apple to transform raw web data into high-quality knowledge used across various apple products.
The data and insights you help generate will directly improve Apple’s foundation models, supporting better understanding, reasoning, and grounding.
Working at the intersection of web data systems, large-scale ML infrastructure, and language modeling, you will shape how Apple builds and leverages knowledge at global scale.
Eeo Content
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.
