Machine Learning Engineer, ML/GenAI Evaluation

Benefits

Parental leave

Not verified

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

$172K-$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

112% 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

Machine Learning Engineer, ML/GenAI Evaluation Austin, United States of America Would you like to contribute to Machine Learning and Generative AI technologies? Are you passionate about measuring what matters and ensuring AI systems work reliably for everyone? Do you believe that rigorous evaluation - including holding models accountable to fairness standards - is what separates great ML from good ML? We truly believe it is! We are defining what exceptional looks like for machine learning across Wallet, Payments, and Commerce. As a Machine Learning Engineer specializing in Evaluation, you will establish the evaluation criteria, metrics frameworks, and quality standards that determine when models are ready to reach hundreds of millions of users. Your judgment shapes model quality and earns the confidence to ship. You'll work at the intersection of rigorous ML science and high-impact product decisions, collaborating closely with ML Engineering, Product, Privacy, and Legal teams. This unique opportunity puts you at the center of model quality - designing adversarial test strategies, surfacing failure modes before they reach users, and owning the sign-off process that ensures Apple's financial features meet the highest bar for accuracy, robustness, and reliability. The ideal candidate is a rigorous, curious ML practitioner who believes that how you measure a model is just as important as how you train it. You think critically about what metrics actually capture, know how models break in the real world, and hold quality standards others find uncomfortably high - including on dimensions like fairness. You will own the full evaluation

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