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Manufacturing Systems and Infrastructure (MSI) team is an engineering organization under the Product Operations org. MSI is responsible for the design, development, and maintenance of systems tools, services, and applications required to efficiently run manufacturing operations at scale across global factory sites.

As an AI Data Platform Engineer with the MSI team, you will design, build, and operate scalable AI data platforms that enable GenAI, Agentic AI, and Embodied AI solutions across the enterprise. You will develop reusable platform services, data pipelines, and data quality frameworks that transform fragmented enterprise and multimodal data into trusted, AI-ready datasets - combining expertise in AI data platform engineering, data quality, systems engineering, and AI data lifecycle management to accelerate AI innovation.

Description

Design, build, and maintain scalable AI data platforms, services, and APIs that support and enable AI model development and production.

Develop data ingestion, transformation, and publishing pipelines for structured, unstructured, and multimodal data.

Build AI-ready datasets through ground truth creation, data curation, annotation workflows, dataset versioning, and metadata management.

Develop data quality frameworks, validation pipelines, observability, and evaluation metrics to ensure trusted AI datasets.

Design and implement Retrieval-Augmented Generation (RAG) pipelines, embedding workflows, vector database integrations, and metadata services for enterprise AI applications.

Build scalable platform capabilities for managing the end-to-end AI data lifecycle, including ground truth dataset creation, dataset versioning, metadata and lineage management, automated data quality validation, governance, and secure publishing of AI-ready datasets.

Collaborate with AI/ML engineers, software engineers, product teams, and domain experts to define AI data requirements and deliver production-ready data solutions.

Optimize platform scalability, reliability, performance, security, and cost across cloud-native environments.

Drive engineering best practices for AI data architecture, platform design, automation, testing, monitoring, and operational excellence.

Evaluate emerging AI technologies and continuously improve platform capabilities that enable GenAI, agentic AI, and embodied AI solutions.

Preferred Qualifications

Experience building platforms supporting GenAI, Agentic AI, or Embodied AI applications.

Experience with multimodal datasets, knowledge graphs, AI evaluation frameworks, or vector search technologies.

Familiarity with enterprise data governance, lineage, metadata management, and AI compliance.

Experience working with manufacturing, operational, IoT, or industrial data platforms.

Demonstrated ability to lead technical initiatives and mentor engineers.

Minimum Qualifications

Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, or a related field.

5+ Experience designing and building scalable data platforms and distributed systems.

Strong programming skills in Python and SQL, with proficiency in Java or Scala preferred.

Experience with Airflow, Kubeflow, or MLflow to build and orchestrate scalable AI data pipelines.

Experience building scalable batch and streaming data pipelines using Spark (PySpark), Kafka, Airflow, and Ray, with proficiency in Pandas and modern data lake/lakehouse architectures (e.g., Iceberg, Delta Lake).

Hands-on experience with AI data engineering, including ground truth dataset creation, data curation, annotation pipelines, dataset versioning, and metadata management.

Experience implementing data validation, quality frameworks, observability, and AI dataset evaluation.

Knowledge of RAG architectures, embedding generation, vector databases, and AI data preparation for LLMs and agentic AI.

Experience with cloud platforms (AWS, Azure, or GCP), Kubernetes, Docker, CI/CD, and Infrastructure as Code.

Strong understanding of distributed systems, APIs, microservices, and enterprise integration patterns.

Excellent communication, collaboration, and technical leadership skills.

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 $150,400 and $277,600, 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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