below marketmost similar roles pay hereabove market
This role pays more than
84%
of similar roles. Most pay
$180,875–$269,775
— the blue band above.
At the midpoint, this role pays about
$297k
versus about
$225k
for comparable roles.
Based on 240 similar postings.
Employer
About Apple Inc
Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
Apple Inc currently has
3497 open roles
on FindRole.
Listed pay typically runs
$174,149–$277,600
across 2702 roles with salary data.
The Engineering Manager, AI Data Platforms & Quality joins the Manufacturing Systems and Infrastructure team to lead a high-performing engineering group. This role involves defining the technical strategy and architecture for next-generation AI data products and platforms that support GenAI, agentic AI, and embodied AI initiatives. You will build scalable capabilities for data ingestion, curation, validation, quality management, metadata, and governance. Key responsibilities include developing AI-ready datasets, RAG pipelines, embeddings, and vector search systems. The role requires expertise in Python, SQL, Spark, Airflow, Kafka, and distributed systems within cloud-based data lakehouse architectures. You will solve complex problems by transforming enterprise and multimodal data into trusted assets, ensuring reliability, observability, and security for automated workflows and intelligent applications across global manufacturing operations.
What does a Engineering Manager earn in California?
Median $279400 from 84 postings across 24 companies.
Define the technical vision, architecture, and roadmap for scalable AI data platforms.
Build reusable platform capabilities for data ingestion, curation, validation, and governance.
Drive the development of AI-ready datasets for GenAI, agentic workflows, and embodied AI.
Enable AI applications through RAG pipelines, embeddings, vector search, and knowledge systems.
Establish engineering best practices for scalability, reliability, observability, and security.
Lead and grow a high-performing engineering team focused on AI data products and quality.
Mentor engineers and develop technical talent to foster a culture of engineering excellence.
What we're looking for
Experience leading solution and data engineering teams building large-scale, production-grade systems.
Strong background in data engineering, distributed systems, and cloud-based data platforms.
Proven experience designing and building scalable data products, data pipelines, APIs, and platform services.
Experience with AI data lifecycle management, including dataset curation, validation, quality evaluation, metadata management, lineage, and governance.
Expertise in Python, SQL, Spark, Airflow, Kafka, data pipelines, distributed systems, and modern data lake/lakehouse architectures.
Experience building AI Data Curation & Quality capabilities, including dataset engineering, validation frameworks, data profiling, observability, and data quality metrics.
Understanding of GenAI enablement technologies, including LLM applications, RAG architectures, embeddings, and AI agent workflows.
Experience with cloud and infrastructure technologies, including AWS/GCP/Azure, Kubernetes, Docker, CI/CD, and scalable production systems.
Experience building AI data platforms or infrastructure supporting LLM applications and AI products (preferred).
Experience with multimodal data including text, image, video, sensor, or operational datasets (preferred).
Experience building data quality frameworks, AI evaluation pipelines, or dataset management platforms (preferred).
Experience translating emerging AI technologies into scalable enterprise solutions (preferred).
Strong communication skills with the ability to influence technical direction across organizations (preferred).