Software Qa Engineer - Automation, Siri

Apple Apple · Big Tech · San Francisco Bay Area, CA +1 · Software and Services

Software QA Engineer focused on automation for Siri, Apple's AI assistant. The role involves ensuring software frameworks and environments are updated for new AI capabilities, partnering with development teams, and owning automation support. Requires expertise in Python, Bash, or Swift with ML/NLP exposure, and experience building test frameworks using ML models and LLMs.

What you'd actually do

  1. You will ensure that our software frameworks and environments are updated and modernized to adapt to the new architectures and usage scenarios of new products in the development pipeline.
  2. You'll also partner closely with product development teams and quality engineering groups as the owner of automation support.
  3. Develop and maintain robust testing frameworks using machine learning models
  4. Expertise with LLM usage to innovate and improve efficiency of the daily work

Skills

Required

  • 5-10+ years of experience working as a Software Quality engineer with primary focus on automation
  • Expertise in Python, Bash and/or Swift with exposure to ML/NLP libraries
  • In-depth knowledge of software development lifecycle, testing methodologies, and testing tools
  • Developing test plans, assessing risk, filing appropriate defects, and providing relevant data for test reporting
  • Develop and maintain robust testing frameworks using machine learning models
  • Experience with developing test strategies, including: writing test plans, test cases, and testing architectures
  • Expertise with LLM usage to innovate and improve efficiency of the daily work
  • BS/MS or equivalent experience in Computer Science or related field

Nice to have

  • Strong software engineering skills, including system design, development, testing, debugging, release and maintenance
  • Drive development and deployment of relevant ML testing tools and infrastructure
  • Deep understanding of automated software testing methodologies and lifecycle, including integration testing, component mocking, and dependency injection
  • Ability to work independently, raise issues and take corrective actions
  • Ability to triage problems, prioritize accordingly, and propose a resolution

What the JD emphasized

  • owner of automation support
  • pioneering AI products
  • robust testing frameworks using machine learning models
  • LLM usage

Other signals

  • AI assistant experiences
  • Apple Intelligence
  • personal context understanding
  • on-screen awareness
  • next-generation hardware platforms
  • pioneering AI products
  • LLM usage