Software Engineering Manager - Siri Evaluation, Test Development Tooling

Apple Apple · Big Tech · Cupertino, CA +1 · Machine Learning and AI

Software Engineering Manager for Siri's Agentic Evaluation Engineering organization, focusing on building tools and AI-powered automation for creating and maintaining evaluation scenarios. The role involves leading a team, setting technical direction, and owning systems for scenario generation, validation, and management to ensure Siri's quality signal drives product decisions.

What you'd actually do

  1. Lead and grow a team of engineers building AI agent-based scenario authoring and selection tools.
  2. Set the team's technical direction and roadmap.
  3. Own the systems that generate, validate, ground, and manage evaluation scenarios through their full lifecycle.
  4. Drive coverage traceability and gap analysis so that every product capability maps to a scenario, and every gap is visible.
  5. Deliver and evolve coverage, reliability, and health dashboards and release-readiness signals that leadership relies on for ship decisions.

Skills

Required

  • 5+ years of experience developing production software (e.g., Swift, Python, Java, or Go).
  • 3+ years of engineering management experience.
  • Strong technical leadership and the ability to set architectural direction and inspire a team.
  • Experience with test infrastructure, CI/CD, or evaluation/ML platforms.
  • Proactive and self-motivated, with demonstrated creative and critical-thinking abilities.

Nice to have

  • Experience building or leading teams that apply LLMs / AI agents to real engineering workflows (e.g., automated test generation, code generation, or agentic pipelines with human-in-the-loop review).
  • Depth in one or more of: distributed systems and backend services (REST/gRPC), cloud infrastructure (Docker/Kubernetes), data platforms, or large-scale test and release infrastructure.
  • Familiarity with Apple's testing frameworks (XCTest and related) and with ML/LLM evaluation concepts.
  • Demonstrated cross-functional leadership and the ability to drive coverage and quality decisions with senior stakeholders.
  • Experience hiring and growing a team, including senior engineers.
  • Excellent spoken and written communication skills.
  • Comfort operating in ambiguity and reshaping team scope as the domain matures.

What the JD emphasized

  • AI agent-based scenario authoring
  • AI agent-based scenario authoring
  • LLMs / AI agents to real engineering workflows
  • ML/LLM evaluation concepts

Other signals

  • AI-powered automation
  • AI agent-based scenario authoring
  • LLMs / AI agents to real engineering workflows
  • ML/LLM evaluation concepts