AIML - Data Scientist, Evaluation

Cupertino, California, United States Machine Learning and AI

Do you get excited by driving product impact via measurement and evaluation, for products and services used by hundreds of millions of people globally? The vision for the AIML Evaluation organization is to improve products by using data as the voice of our customers. Within this organization the mission of the Data Science and Insight team is to inform product evolution through measurement, evaluation, and analysis of the user experience.

You will partner with Apple Intelligence engineering teams to improve product quality and guide feature development with data, to deliver amazing experiences across iPhone, iPad, Home Pod, Mac, Apple Watch, Apple tv, across dozens of languages.

Research and develop evaluation methods to improve the quality of Apple Intelligence user facing products. Work with evaluation/experimentation engineering teams to get your methodological developments translated into technologies that Apple Intelligence engineering will use every day.

Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying advanced analytical methods as needed. Conduct analysis that includes data gathering and requirements specification, processing, analysis, ongoing work, and presentations.

Build and prototype analysis pipelines iteratively to provide insights elop comprehensive knowledge of Siri Search data structures and metrics, advocating for changes where needed for product development.

Partner closely with Apple Intelligence search engineering teams on core machine learning algorithms and systems that are part of product’s ability to understand and respond to requests.

You should be passionate about building outstanding products. This position involves a wide variety of skills and innovation.

• 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.
• 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.
• 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.

• Experience in data science, machine learning, and analytics, including statistical data analysis and A/B testing.
• Experience articulating and translating business questions and using statistical techniques to arrive at an answer using available data.
• Strong programming skills, including data-querying skills (SQL and/or Spark, etc.) and experience with a scripting language for data processing and development (e.g., Python, R, or Scala).
• Excellent collaboration skills to achieve impactful results by working effectively with diverse cross-functional teams, including PMs, engineers, data scientists, and others.
• B.S. in Machine Learning, Computer Science, Statistics, Operations Research or other quantitative fields.

• Applicants have a good understanding of large language model (LLMs), including their architecture, training methods, prompt engineering and fine-tuning for specific tasks.
• Hands-on experience in applying LLMs to solve technical problems, such as data analysis, data automation, synthetic data generation, with proven ability to optimize model performance for accuracy and efficiency.
• Ph.D. in machine learning, computer science, statistics, operations research or other quantitative fields.
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