ML Engineer - Evaluation Analysis, Metric and Data Strategy

Apple Apple · Big Tech · Culver City +2 · Software and Services

ML Engineer focused on defining and analyzing quality metrics for AI-powered features in consumer productivity and creative applications. This role is critical for informing model development, feature launches, and product strategy by translating evaluation data and user behavior into actionable insights. It involves designing metrics frameworks, auditing data representativeness, and developing evaluation methods for complex, agentic AI experiences.

What you'd actually do

  1. Define and own the quality metrics framework across AI features and agentic experiences, ensuring each feature has a clear north-star metric and supporting diagnostics
  2. Analyze evaluation outputs to identify quality trends, regressions, and segment-level patterns across both single-turn and multi-turn interactions, tracking how quality degrades or holds over extended conversations
  3. Drive the data collection strategy with partner teams
  4. Ensure evaluation data stays grounded in real-world user behavior
  5. Audit evaluation data representativeness to verify that datasets reflect actual user distributions

Skills

Required

  • Python (pandas, scipy, scikit-learn) or R for data analysis and visualization
  • statistical analysis methods including significance testing, sampling design, effect size estimation, and experimental design
  • working with production user data, understanding its biases and limitations
  • design evaluation approaches where the unit of analysis is a session or conversation rather than a single model output
  • Track record of independently designing metrics frameworks and driving data-informed decisions across cross-functional teams

Nice to have

  • Experience designing evaluation or quality metrics for AI-powered or ML-driven features in consumer-facing products
  • Familiarity with productivity software or creative applications
  • Experience partnering with engineering or data teams to define data collection requirements and schemas
  • Track record of translating complex analytical findings into concise recommendations for non-technical decision-makers
  • Experience evaluating tool-use accuracy, retrieval quality, or function-calling reliability within AI systems
  • Experience with evaluation methodology including inter-annotator agreement, evaluation bias detection, and dataset representativeness auditing
  • Familiarity with agentic orchestration frameworks (LangChain, LangGraph, CrewAI, AutoGen) and emerging agent interoperability protocols (A2A, MCP), with an understanding of how architectural choices in agent design affect evaluability
  • Understanding of ML model development processes, with the ability to specify what evaluation signals are useful for model improvement
  • Experience managing evaluation across multiple features or product areas simultaneously, with systematic rather than ad-hoc approaches
  • Graduate degree in a relevant quantitative field

What the JD emphasized

  • define how AI feature quality is measured
  • define what “quality” means when the unit of evaluation is a conversation
  • Track record of independently designing metrics frameworks and driving data-informed decisions across cross-functional teams

Other signals

  • defining quality metrics for AI features
  • analyzing evaluation signals and user behavior
  • influencing product direction based on AI quality