Job description

At Apple Ads, we are building the next generation of privacy-focused advertising capabilities. In the Data Governance team, we work at the cutting edge of data engineering, machine learning, and privacy at Apple's scale. We are constantly developing data and privacy management products to provide amazing user experiences and to drive value for developers and partners. The team is seeking a Data Governance Engineer to support our data governance objectives. You will play a crucial role in delivering on Apple's privacy commitments to our customers. You will partner across our engineering, product, privacy and reliability organizations to deliver on data access, use, protection, minimization, retention, and other data governance execution areas. B.S. in Computer Science or related field with 5+ years of software development experience, including exposure to ML/AI applications Production experience in Python, Java, Rust or similar languages, with familiarity in data processing and API development Understanding of distributed systems and cloud platforms, including CAP theorem tradeoffs and basic ML model deployment Experience with containerization (Docker), orchestration (Kubernetes), and infrastructure as code (e.g. Terraform, CloudFormation) Proficiency in CI/CD pipelines and DevOps practices using Git, GitHub Actions/Jenkins/GitLab CI, with experience in automated testing and deployment workflows Familiarity with observability and monitoring tools (e.g. Prometheus, Grafana, DataDog) and logging frameworks for production systems Basic knowledge of machine learning concepts and MLOps, including data pipelines, model versioning, and experiment tracking tools Expertise in open source data analytics and governance platforms: architecture, deployment, and performance tuning of Datahub, Apache Spark, Flink, Hive, Hadoop/HDFS, and Iceberg Rest Catalog Experience building multi-agent AI systems: proficiency with LangChain, LangGraph, or AutoGen frameworks; strong prompt engineering and LLM integration skills; ability to design event-driven architectures for autonomous workflows Skills in integration and communication layers: implement MCP servers and APIs using Python, REST/GraphQL, and message queuing (e.g. Kafka, RabbitMQ); experience with modern data platforms including Snowflake, Databricks, and vector databases MLOps and observability capabilities: deploy containerized AI systems with comprehensive monitoring; track experiments using MLflow or Weights & Biases; implement distributed tracing for agent workflows and model performance

Requirements

Must-haves

B.S. in Computer Science or related field with 5+ years of software development experience, including exposure to ML/AI applications
Production experience in Python, Java, Rust or similar languages, with familiarity in data processing and API development
Understanding of distributed systems and cloud platforms, including CAP theorem tradeoffs and basic ML model deployment
Experience with containerization (Docker), orchestration (Kubernetes), and infrastructure as code (e.g. Terraform, CloudFormation)
Proficiency in CI/CD pipelines and DevOps practices using Git, GitHub Actions/Jenkins/GitLab CI, with experience in automated testing and deployment workflows
Familiarity with observability and monitoring tools (e.g. Prometheus, Grafana, DataDog) and logging frameworks for production systems
Basic knowledge of machine learning concepts and MLOps, including data pipelines, model versioning, and experiment tracking tools
Expertise in open source data analytics and governance platforms: architecture, deployment, and performance tuning of Datahub, Apache Spark, Flink, Hive, Hadoop/HDFS, and Iceberg Rest Catalog

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlaipythonjavarustapidockerkubernetesci/cdrestllmgraphql

Platform: apple
Effort: medium