Software Engineering Manager, Siri AI Quality

Apple Apple · Big Tech · Cupertino, CA +1 · Software and Services

Software Engineering Manager for Siri AI Quality at Apple, leading a team to evaluate and ensure the quality of Siri's AI systems, focusing on user interaction, on-device models, and building automation frameworks. The role involves partnering with ML and engineering teams to define quality strategies, adopt agentic AI systems for test generation, and report on quality metrics for a large-scale consumer product.

What you'd actually do

  1. Lead a software quality engineering team to evaluate how users initiate and interact with Siri. across all platforms (iPhone, iPad, Mac, CarPlay, Watch).
  2. Define and execute the end-to-end qualification strategy for Siri’s attention and invocation experiences, as well as the functional validation of Siri’s on-device models.
  3. Partner deeply with software, machine learning, and speech/audio engineering teams to create a quality strategy that integrates early in the development lifecycle (shift-left).
  4. Drive team efficiency by adopting agentic AI systems and advanced coding tools to accelerate test generation, automate triage, and improve auto-error-attribution.
  5. Build and scale robust test automation frameworks, evaluation pipelines, and client integration frameworks to validate complex AI interactions.

Skills

Required

  • BS or MS in Computer Science, Machine Learning, or a related technical field.
  • 3+ years of engineering leadership or direct team management experience.
  • Experience in software quality automation, test engineering, or machine learning evaluation.
  • Demonstrated experience building, testing, and shipping consumer-facing software products at scale.
  • Proven track record of forming partnerships with cross-functional teams to solve complex technical problems.

Nice to have

  • Strong history of delivering innovation, specifically utilizing agentic AI tools and advanced automation to improve engineering efficiency.
  • Deep understanding of modern software lifecycles, test engineering best practices, and shift-left quality methodologies.
  • Domain experience with assistant-based products, natural language processing, or global customer bases.
  • Ability to execute broad strategies at scale while maintaining a strong grasp of technical details to guide the team.
  • Excellent communication skills with the ability to clearly present quality metrics and risk assessments to leadership.

What the JD emphasized

  • agentic AI systems
  • advanced coding tools
  • test generation
  • automate triage
  • shift testing left
  • continuous evaluation
  • on device models
  • functional validation
  • quality strategy
  • AI systems

Other signals

  • leading a team
  • evaluating AI systems
  • building automation frameworks
  • shipping consumer-facing products