AIML Sr Data Engineer, DMLI

Are you excited to tackle some of the most ambitious technical challenges in Apple Intelligence Be involved in collaborating closely with our machine learning researchers engineers and data scientists Together you will orchestrate groundbreaking research initiatives and develop transformative products designed to build a significant impact for billions of users worldwide!nnThe AI and Machine Learning team is looking for Senior Data Engineer to build world-class data infrastructure and solutions that are used by data scientists ML engineers and researchers to power Apple Foundation Model lifecycle. nn

We are looking for a skilled Senior Data Engineer to join our engineering this role you will be responsible for designing building and maintaining scalable data pipelines data platforms and data integration solutions that enable reliable analytics machine learning and business intelligence capabilities across the

Design build and optimize large-scale batch and streaming data pipelines using Google Cloud Dataflow (Apache Beam) and related and maintain data models and transformations in BigQuery to support analytics reporting and ML use scalable and reliable data architectures using GCP services such as Pub/Sub Dataflow BigQuery Cloud Storage Dataproc and data from multiple internal and external systems while ensuring data quality consistency lineage and closely with data scientists ML engineers analysts and product teams to deliver production-grade data robust ETL/ELT workflows and orchestration pipelines using cloud-native troubleshoot and optimize data pipelines for performance cost efficiency and best practices for data security access control and compliance in cloud

Bachelors or Masters degree in Computer Science Engineering or a related technical field (or equivalent practical experience).nStrong software engineering foundation with 5 years of experience in data engineering backend engineering or distributed hands-on experience designing and building large-scale batch and streaming data pipelines using Python and distributed systems. nExpertise in ETL/ELT development event-driven architectures and working with high-volume datasets using technologies such as Pub/Sub Dataflow (Apache Beam) and Spark-based processing (Dataproc or equivalent).nDeep expertise in the Google Cloud Platform data ecosystem including BigQuery Dataflow Pub/Sub and Composer (Airflow). Proven ability to design optimize and operate scalable data platforms with strong experience in BigQuery performance tuning cost optimization data modeling partitioning clustering and query optimization at in Python version control CI/CD practices testing and code reviews. nnnn

7 years of experience in data engineering or large-scale distributed designing end-to-end data platforms or data mesh understanding of data governance data lineage and metadata management supporting ML pipelines and MLOps workflows including feature engineering and training data building systems with strong reliability observability and SLAs (monitoring alerting debugging distributed pipelines).nFamiliarity with Generative AI / LLM-based systemsincluding: LLM-powered data workflows Agentic pipelines Embedding/vector-based retrieval influencing architecture decisions across teams and driving technical collaborating in cross-functional fast-paced tech environments or cloud-native organizations with a focus on building reliable maintainable and production-grade data