AI Data Platform Engineer for GenAI & Multimodal Pipelines

Socket.dev; Cupertino (CA); On-site; USD 170,000 - 250,000; Full time

Job summary

Apple seeks an AI Data Platform Engineer to design, build, and operate scalable AI data platforms supporting GenAI, agentic AI, and embodied AI across the enterprise. You’ll create data pipelines, metadata management, and data quality frameworks that transform diverse data into trusted AI-ready datasets.

Collaborate with AI/ML engineers, product teams, and domain experts to deliver production-ready data solutions, optimize platform reliability, security, and cost across cloud environments.

Qualifications

• Bachelor’s or Master’s in CS/Software/Data Engineering or related field.
• 5+ years designing scalable data platforms and distributed systems.
• Strong Python and SQL, plus Java/Scala preferred.
• Experience with Airflow, Kubeflow, MLflow for data pipelines.
• Batch and streaming data pipelines using Spark, Kafka, Airflow.
• Hands-on AI data engineering, dataset versioning, and metadata management.
• Knowledge of RAG, embeddings, vector databases, and LLM prep.
• Cloud platforms (AWS/Azure/GCP), Kubernetes, Docker, CI/CD, IaC.

Responsibilities

• Design, build, maintain scalable AI data platforms and APIs.
• Create data pipelines for structured, unstructured, and multimodal data.
• Develop ground truth data, dataset versioning, and metadata management.
• Implement data quality, observability, and evaluation pipelines.
• Design Retrieval-Augmented Generation workflows and vector DBs.
• Collaborate with AI/ML teams, optimize cloud-native platform performance.

Skills

Python

SQL

Distributed systems

APIs & microservices

Communication

Leadership

Education

Bachelor's degree in CS/Software Engineering/Data Engineering

Master's degree (preferred)

Tools

Airflow

Kubeflow

MLflow

Spark (PySpark)

Kafka

Pandas

Iceberg/Delta Lake

Kubernetes

Docker

CI/CD

Job description

Apple seeks an AI Data Platform Engineer to design, build, and operate scalable AI data platforms supporting GenAI, agentic AI, and embodied AI across the enterprise. You’ll create data pipelines, metadata management, and data quality frameworks that transform diverse data into trusted AI-ready datasets.

Collaborate with AI/ML engineers, product teams, and domain experts to deliver production-ready data solutions, optimize platform reliability, security, and cost across cloud environments.