About The Position

Apple is where individual imaginations gather together, committing to the values that lead to phenomenal work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other's ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It's the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you'll do more than join something - you'll add something. As part of Siri AI Quality Engineering, we are dedicated to creating groundbreaking conversational assistant technologies for both large-scale systems and new client devices, building upon our legacy of intelligent assistant solutions that already assist millions of users worldwide. Does the opportunity to play a part in building groundbreaking technology for large-scale systems, natural language and artificial intelligence excite you? Do you want to expand the experience of Siri and other AI/ML products to new products that will help millions get things done, across the globe? Join Siri AI Quality Engineering at Apple and contribute to a highly accomplished team dedicated to releasing high-quality software, models, and products!

Requirements

  • Deep understanding of large scale data validation platforms with a focus on privacy.
  • Experience building and deploying applications with Kubernetes.
  • Knowledge of statistics-based evaluation approaches, ML training pipelines, and techniques for enhancing the accuracy of ML systems.
  • Strong attention to detail and proven track record of delving into data, uncovering hidden patterns, and conducting comprehensive error/deviation analysis.

Responsibilities

  • Design, build, and evolve evaluation environments for AI assistant products.
  • Create tools and frameworks for instrumentation and privacy evaluation.
  • Ensure AI products meet privacy promises and are instrumented for trustworthy measurement.
  • Collaborate with data and product engineering teams to provide evaluation methodologies and automation frameworks within a micro-services architecture.
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