AI Data Engineer Jobs at Apple with Visa Sponsorship
AI Data Engineer jobs at Apple sit at the intersection of machine learning infrastructure and large-scale data systems, supporting products used by hundreds of millions of people. Apple has an established sponsorship track record across multiple visa categories, making it a realistic target for international candidates with the right technical background.
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Get Access To All JobsINTRODUCTION
Imagine what you could do here. At Apple, new ideas have a way of becoming outstanding products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.
Apple’s Sales organization generates the revenue needed to fuel our ongoing development of products and services.
Apple's US Sales Technology Team is looking for a talented individual who is passionate about crafting, implementing, and operating solutions that have a direct and measurable impact on Apple Sales and its customers. We also leverage Artificial Intelligence and Machine Learning (AIML) to enhance our sales processes, and this role will be critical in building the data infrastructure to support those initiatives.
Description
As an AI Data & Knowledge Engineer, you will develop infrastructure, systems, services, and tools for automating sales processes. We’re looking for an exceptional engineer that lives at the intersection of development, operations, data, and systems engineering to build solutions for large-scale continuous data transformation and delivery. This role will specifically focus on building and maintaining data pipelines for both structured and unstructured data, enabling the development and deployment of AIML models.
Responsibilities
- Responsible for the development and design of data pipelines and data knowledge layers for agentic AI applications.
- Design and implement data models for a semantic layer that integrates analytics data from multiple sources in an efficient and effective manner.
- Design and build scalable data and knowledge layers that power chatbots and other agentic applications.
- Build RAG-ready data pipelines and knowledge layers encompassing document ingestion, parsing, metadata tagging, embeddings, indexing with vector search.
- Design scalable architecture for semantic and hybrid search, knowledge graphs to enable contextually accurate text-to-SQL generation.
- Build mechanisms for incremental synchronization of data to knowledge updates so agent responses are current and reliable.
- Designing and operating distributed data systems - from SQL/NoSQL databases, Vector search, and orchestration.
- Collaborate with Analytics and Data Science teams to translate business requirements into reliable, actionable knowledge layers that support AI agent development and deliver targeted business outcomes.
- Collaborate with internal business partners, internal technology resources (database, system, networking), external vendors, and partners.
- Play an active role in the development and maintenance of user documentation, including data models, mapping rules, and data dictionaries.
- Ensure data quality and accuracy by developing data validation and reconciliation processes.
- Build and maintain data pipelines for ingesting, processing, and transforming unstructured data sources, such as customer feedback, social media data, or sales call recordings.
- Develop data quality monitoring and validation processes specifically for AIML datasets, including identifying and addressing data bias.
- Work with data scientists to understand data requirements for AIML model training and deployment, ensuring data is available in the appropriate format and quality.
- Implement data governance policies and procedures to ensure the responsible and ethical use of data in AIML applications.
MINIMUM QUALIFICATIONS
- Experience designing and building knowledge layers for AI systems, including knowledge graphs, RAG pipelines, and vector databases to ground LLM-driven applications in accurate, structured, unstructured and retrievable enterprise knowledge.
- Experience modeling enterprise knowledge and metadata within semantic layers to represent business entities, attributes, and their relationships.
- 5+ years of experience in designing, building, and maintaining scalable data solutions for large-scale analytics.
- Proficiency in SQL and development experience with cloud database environments like Snowflake, Redshift, Databricks.
- Proficiency in programming languages like Python, Java, R and open-source frameworks for distributed processing like Hadoop and Spark.
- Experience building data pipelines to ingest, transform, and continuously synchronize structured and unstructured enterprise data from multiple sources.
- Hands-on experience using development tools in a modern cloud data stack for code management, versioning using Git, CI/CD tools, automation and orchestration using Apache Airflow or others and monitoring & alerting.
- Experience with Cloud platforms AWS, Azure or Google Cloud.
PREFERRED QUALIFICATIONS
- Experience architecting and developing data pipelines through ETL tools, API integration with on-premise and cloud-based sources.
- Experience building ontology-based semantic layer including a business ontology of sales concepts, a technical ontology of data sources and schemas, and execution traces that provide feedback for continuous improvement.
- Strong understanding of LLM evaluation and AI quality tooling, retrieval metrics, and observability to improve application reliability.
- Experience working with unstructured and Semi-structured data sets (e.g., JSON, Parquet, PDF, text, images, audio, video).
- Experience with data governance and observability tools; for example DataHub, Collibra.
- Experience articulating and translating business questions into data solutions and proven ability to lead development projects from start to finish.
- Broad knowledge of web standards relating to REST, HTTP, JSON, etc.
- Experience with data labeling and annotation tools and processes.
- Familiarity with AI/ML model development lifecycle and data needs for training and deployment.
- Ability to balance competing priorities, long-term projects, and ad hoc requirements.
- Ability to work in a fast-paced, dynamic, constantly evolving business environment.
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 $149,200 and $249,000, 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.
INTRODUCTION
Imagine what you could do here. At Apple, new ideas have a way of becoming outstanding products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.
Apple’s Sales organization generates the revenue needed to fuel our ongoing development of products and services.
Apple's US Sales Technology Team is looking for a talented individual who is passionate about crafting, implementing, and operating solutions that have a direct and measurable impact on Apple Sales and its customers. We also leverage Artificial Intelligence and Machine Learning (AIML) to enhance our sales processes, and this role will be critical in building the data infrastructure to support those initiatives.
Description
As an AI Data & Knowledge Engineer, you will develop infrastructure, systems, services, and tools for automating sales processes. We’re looking for an exceptional engineer that lives at the intersection of development, operations, data, and systems engineering to build solutions for large-scale continuous data transformation and delivery. This role will specifically focus on building and maintaining data pipelines for both structured and unstructured data, enabling the development and deployment of AIML models.
Responsibilities
- Responsible for the development and design of data pipelines and data knowledge layers for agentic AI applications.
- Design and implement data models for a semantic layer that integrates analytics data from multiple sources in an efficient and effective manner.
- Design and build scalable data and knowledge layers that power chatbots and other agentic applications.
- Build RAG-ready data pipelines and knowledge layers encompassing document ingestion, parsing, metadata tagging, embeddings, indexing with vector search.
- Design scalable architecture for semantic and hybrid search, knowledge graphs to enable contextually accurate text-to-SQL generation.
- Build mechanisms for incremental synchronization of data to knowledge updates so agent responses are current and reliable.
- Designing and operating distributed data systems - from SQL/NoSQL databases, Vector search, and orchestration.
- Collaborate with Analytics and Data Science teams to translate business requirements into reliable, actionable knowledge layers that support AI agent development and deliver targeted business outcomes.
- Collaborate with internal business partners, internal technology resources (database, system, networking), external vendors, and partners.
- Play an active role in the development and maintenance of user documentation, including data models, mapping rules, and data dictionaries.
- Ensure data quality and accuracy by developing data validation and reconciliation processes.
- Build and maintain data pipelines for ingesting, processing, and transforming unstructured data sources, such as customer feedback, social media data, or sales call recordings.
- Develop data quality monitoring and validation processes specifically for AIML datasets, including identifying and addressing data bias.
- Work with data scientists to understand data requirements for AIML model training and deployment, ensuring data is available in the appropriate format and quality.
- Implement data governance policies and procedures to ensure the responsible and ethical use of data in AIML applications.
MINIMUM QUALIFICATIONS
- Experience designing and building knowledge layers for AI systems, including knowledge graphs, RAG pipelines, and vector databases to ground LLM-driven applications in accurate, structured, unstructured and retrievable enterprise knowledge.
- Experience modeling enterprise knowledge and metadata within semantic layers to represent business entities, attributes, and their relationships.
- 5+ years of experience in designing, building, and maintaining scalable data solutions for large-scale analytics.
- Proficiency in SQL and development experience with cloud database environments like Snowflake, Redshift, Databricks.
- Proficiency in programming languages like Python, Java, R and open-source frameworks for distributed processing like Hadoop and Spark.
- Experience building data pipelines to ingest, transform, and continuously synchronize structured and unstructured enterprise data from multiple sources.
- Hands-on experience using development tools in a modern cloud data stack for code management, versioning using Git, CI/CD tools, automation and orchestration using Apache Airflow or others and monitoring & alerting.
- Experience with Cloud platforms AWS, Azure or Google Cloud.
PREFERRED QUALIFICATIONS
- Experience architecting and developing data pipelines through ETL tools, API integration with on-premise and cloud-based sources.
- Experience building ontology-based semantic layer including a business ontology of sales concepts, a technical ontology of data sources and schemas, and execution traces that provide feedback for continuous improvement.
- Strong understanding of LLM evaluation and AI quality tooling, retrieval metrics, and observability to improve application reliability.
- Experience working with unstructured and Semi-structured data sets (e.g., JSON, Parquet, PDF, text, images, audio, video).
- Experience with data governance and observability tools; for example DataHub, Collibra.
- Experience articulating and translating business questions into data solutions and proven ability to lead development projects from start to finish.
- Broad knowledge of web standards relating to REST, HTTP, JSON, etc.
- Experience with data labeling and annotation tools and processes.
- Familiarity with AI/ML model development lifecycle and data needs for training and deployment.
- Ability to balance competing priorities, long-term projects, and ad hoc requirements.
- Ability to work in a fast-paced, dynamic, constantly evolving business environment.
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 $149,200 and $249,000, 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.
See all AI Data Engineer Jobs at Apple
Sign up for free to unlock all listings, filter by visa type, and get alerts for new AI Data Engineer Jobs at Apple.
Get Access To All JobsTips for Finding AI Data Engineer Jobs at Apple
Align your portfolio to Apple's ML stack
Apple's AI Data Engineering work centers on on-device intelligence and privacy-preserving ML pipelines. Showcasing experience with distributed data infrastructure, CoreML-adjacent tooling, or federated learning signals direct relevance before your resume reaches a recruiter.
Target roles tied to Apple Intelligence
Apple's generative AI push has concentrated data engineering hiring around its Apple Intelligence initiative. Filtering by teams like Siri, Core ML, and Health AI increases your odds of landing a role with active headcount and sponsorship budget already approved.
Confirm your visa category before the offer stage
Apple sponsors H-1B, E-3, TN, and F-1 OPT among others. Knowing which category fits your nationality and status before negotiations means you can ask the right questions early rather than scrambling once an offer is on the table.
Request premium processing during H-1B filing
USCIS premium processing delivers a decision within 15 business days. For AI Data Engineer roles with a hard start date tied to a product cycle, ask Apple's immigration team upfront whether they'll elect premium to avoid a months-long wait.
Use Migrate Mate to find open AI Data Engineer roles at Apple
Sponsorship-eligible positions aren't always labeled as such on general job boards. Search Migrate Mate to filter Apple's current AI Data Engineer openings by visa type so you're only applying to roles where sponsorship is already confirmed.
Prepare your credentials for specialty occupation review
USCIS scrutinizes whether an AI Data Engineer role qualifies as a specialty occupation under H-1B rules. Gather transcripts, degree equivalency evaluations if your credential is from outside the U.S., and any documentation linking your specific degree field to the role.
Frequently Asked Questions
Does Apple sponsor H-1B visas for AI Data Engineers?
Yes, Apple sponsors H-1B visas for AI Data Engineer roles. Because H-1B is subject to an annual lottery with an 85,000-slot cap, Apple typically files petitions in April for an October 1 start date. Candidates already in H-1B status transferring from another employer can often start sooner through cap-exempt portability provisions.
How do I apply for AI Data Engineer jobs at Apple?
Applications go through Apple's careers portal at jobs.apple.com. The process typically involves a recruiter screen, a technical phone interview focused on data modeling and ML pipeline design, and a virtual or on-site loop with cross-functional teams. Sponsorship discussions happen with the recruiter early in the process, so raise your visa situation before the offer stage to avoid delays.
Which visa types does Apple commonly use for AI Data Engineer roles?
Apple sponsors H-1B for most nationalities, E-3 visa exclusively for Australian citizens, TN visa for Canadian and Mexican nationals in qualifying technical occupations, and F-1 OPT and CPT for students finishing U.S. degrees. For permanent residence, Apple supports EB-2 and EB-3 green card pathways, which typically begin after you've established a track record in the role.
What qualifications does Apple expect for AI Data Engineer roles?
Most AI Data Engineer postings at Apple require a bachelor's degree in computer science, data engineering, or a closely related field, with a master's or PhD preferred for research-adjacent teams. Practical experience with large-scale data pipelines, ML feature stores, and privacy-focused data handling is weighted heavily. Apple's on-device AI focus means familiarity with edge inference constraints is a differentiator.
How do I find AI Data Engineer roles at Apple that offer visa sponsorship?
Not every job listing explicitly states which visa categories an employer will support, which makes filtering difficult on general platforms. Migrate Mate lets you browse AI Data Engineer openings at Apple filtered by the specific visa types Apple sponsors, so you can identify the right roles before investing time in an application.