Senior Applied Scientist - AI Evaluation & Quality Systems

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Mental health support

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Learning budget

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Verification

Not verified last checked Jun 13, 2026

Salary

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401(k) match

Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.

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Market context

Median wage (BLS OEWS)

$111,944 national median

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

78% above the BLS national median for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

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Weekend work

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Application

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Assessment

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Deadline

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Where they hire

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About this role

Senior Applied Scientist - AI Evaluation & Quality Systems Seattle, United States of America Apple Services Engineering (ASE) powers the AI and LLM features behind experiences that hundreds of millions of users love every day. As these systems increasingly rely on human-in-the-loop evaluation, the quality of our products is directly constrained by the quality of our evaluation systems. We believe that to build exceptional AI, you need exceptional mechanisms to validate the signals used to train and evaluate them. The Human-centered AI, Data Quality Operations team is looking for a Senior Applied Scientist to join our growing team. We are building the systems and methodologies that make AI evaluation trustworthy, and scalable - directly shaping how Apple develops and validates AI across products and services. In this role, you will develop novel, scalable quality control solutions, working closely with cross-functional teams to ensure the data powering our AI/ML systems meets the highest standards of accuracy, consistency, and relevance. Your work will span two connected problem spaces. The first is the methodology and tooling that generates reliable ground truth and detects quality failures across human annotation and automated evaluation pipelines. The second is the autonomous QA agents that make those methodologies generalizable across teams and use cases. This role demands fluency across research thinking and engineering execution - you will prototype, validate, and ship. A strong point of view on when not to use a model or agent is as valued here as the ability to build one. Design and implement

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