Senior Software Development Engineer in Test

Apple Apple · Big Tech · Cupertino, CA · Software and Services

Senior Software Development Engineer in Test (SDET) for Apple Services Engineering's AI/ML quality organization, focusing on building developer tools, test frameworks, and libraries for ML pipelines and AI platform services. The role involves defining testing strategies, guiding tool selection, implementing automation, and collaborating with ML Engineers and Data Scientists to ensure quality and governance of ML outputs.

What you'd actually do

  1. Own and define the testing strategy for end-to-end ML pipelines, data flows, and AI platform services.
  2. Guide the selection and integration of tools and platforms that support scalable test automation, data validation, continuous training (CT), and continuous integration/continuous delivery (CI/CD) in ML workflows.
  3. Partner closely with AI/ML engineers, MLOps, and data science teams to ensure testability, model governance, and validation of ML outputs.
  4. Define and enforce standards for quality in ML systems — including unit, integration, regression, and fairness testing.
  5. Define and track quality metrics such as test coverage for ML pipelines, test flakiness, and pipeline reliability.

Skills

Required

  • Java, Python or similar programming languages
  • test frameworks and tools such as PyTest, JUnit, or equivalent
  • designing and implementing testing frameworks for distributed systems, machine learning pipelines, and service-layer testing
  • backend APIs, data processing, and infrastructure components
  • planning and execution of validating REST and gRPC APIs
  • quality engineering
  • testing machine learning algorithms
  • large scale distributed data systems
  • software development and/or test automation
  • leading complex, distributed systems

Nice to have

  • Knowledge of Big Data systems, Apache Spark
  • testing or working with AI/ML systems or platforms that include ML model training or data pipelines and algorithms
  • building tooling, automating tasks, and developing supporting systems

What the JD emphasized

  • testing strategy for end-to-end ML pipelines
  • test automation
  • ML workflows
  • quality assurance best practices
  • ML systems
  • quality metrics for ML pipelines

Other signals

  • testing strategy for ML pipelines
  • test automation for ML workflows
  • quality assurance for ML systems