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AIML - Senior Manager, Evaluation - Data Science & Insights

Seattle

Pay
Pay not listedSimilar roles state $153K–$225K
Work setting
Not stated
Employment
Not stated

What you’ll work on

Full posting
  • Lead a team that evaluates Apple Intelligence and Siri AI features

  • Use autograders and rubrics to drive improvements in models and agentic features

From the employer’s posting
to lead a team within our Data Science and Insights organization
using these tools to proactively drive improvements in models and agentic features

What you’ll bring

All qualifications

Core experience

  • Strong experience in traditional human evaluation methodology
  • Hands-on experience building and deploying LLM-based autograders and rubrics
Qualification wording
strong experience in traditional human evaluation methodology
hands-on experience building and deploying LLM-based autograders and rubrics

Posting record

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Status in our records
Live
First seen by us
Aug 27, 2026 · 64 days ago
Recorded sightings
385
Last seen by us
Oct 31, 2026
Employer says posted
Aug 19, 2026

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Job description

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Apple is where individual imaginations gather together, committing to the values that lead to great 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. Apple's AIML Evaluation team is looking for a seasoned, technical leader to lead a team within our Data Science and Insights organization. The organization leads Evaluation for Apple Intelligence, Siri AI and a large portfolio of other billion+ user facing features in SWE. Successful candidates will have strong experience in traditional human evaluation methodology, in addition to hands-on experience building and deploying LLM-based autograders and rubrics, and using these tools to proactively drive improvements in models and agentic features.
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Seattle

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