Staff Applied Scientist, AI Quality & Meta Evaluation

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

Not verified checked Jun 13, 2026

Salary

$201K-$302K not verified - source not recorded; timestamp not recorded

401(k) match

Reported from DOL Form 5500 industry filing (not employer-specific)

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

125% above the BLS role benchmark 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

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Staff Applied Scientist, AI Quality & Meta Evaluation Seattle, United States of America Apple Services Engineering (ASE) powers AI and LLM features across App Store, Music, Video, and more. As these systems increasingly rely on LLM Judges and automated evaluators to score model performance at scale, the trustworthiness of those evaluation signals becomes mission-critical. We believe that to build exceptional LLMs, you need exceptional mechanisms to validate the signals used to train and evaluate them. As a Principal Applied Scientist on the Human Centered AI team, you will be the technical engine behind our Data Quality Validation framework. This is a high-impact individual contributor role for a scientist who wants to architect and build - not just advise. You will own the data science methodology underpinning our data quality validation models, design the statistical frameworks that govern judge reliability, and work hands-on to close the loop between automated evaluation and human ground truth. You will be the person who answers the hardest question in our stack: "Can we trust the evaluators that are evaluating our models?" Design, develop, and iterate on the reasoning agent that serves as our adjudicator, auditing Production LLM Judge outputs for hallucination, drift, and systematic bias Develop the statistical and ML approaches that detect when Production LLM Judges diverge from ground truth, including confidence calibration, entropy-based uncertainty quantification, and out-of-distribution detection Define the algorithms that determine what gets routed for deeper review, moving the team from random sampling to principled, risk-stratified smart sampling Design the hierarchical

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