Aiml - Data Scientist, Evaluation

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

This role focuses on designing and implementing evaluation frameworks for AI/ML systems, specifically for Apple's consumer-facing products. The Data Scientist will work with large datasets, develop methodologies for assessing product quality, and partner with engineering teams to improve user experience and guide feature development. The role involves building evaluation datasets, human-in-the-loop systems, and translating insights into actionable recommendations.

What you'd actually do

  1. Design and Own End-to-End Evaluation Frameworks: Develop rigorous evaluation methodologies for AI/ML systems, including metric definition, sampling strategy, experiment design, and statistical validity checks. Build scalable pipelines that ensure trustworthy, reproducible, and interpretable results across product surfaces and model iterations.
  2. Build High-Quality Evaluation Datasets & Human-in-the-Loop Systems: Create and maintain gold-standard datasets for offline and online model assessment. Lead data generation and annotation workflows (e.g., human ratings, Red Teaming, preference data, domain-specific evals), ensuring coverage, data quality, bias mitigation, and alignment with product and safety goals.
  3. Partner Cross-Functionally to Drive Model & Product Decision-Making: Translate evaluation insights into actionable recommendations for model training, ranking, and product launches. Collaborate closely with Research, Engineering, Product, and Safety teams to define quality bars, monitor regressions, optimize user experience, and guide roadmap prioritization.

Skills

Required

  • data science
  • machine learning
  • analytics
  • statistical data analysis
  • A/B testing
  • SQL
  • Spark
  • Python
  • R
  • Scala
  • collaboration skills

Nice to have

  • large language models (LLMs)
  • LLM architecture
  • LLM training methods
  • prompt engineering
  • fine-tuning
  • LLM application to technical problems
  • data analysis automation
  • synthetic data generation
  • model performance optimization
  • Ph.D.
  • 5 years of relevant work experience

What the JD emphasized

  • evaluation methods
  • evaluation frameworks
  • evaluation insights

Other signals

  • evaluation frameworks
  • large datasets
  • user experience
  • machine learning algorithms