Machine Learning Engineer - AI & ML Evaluation Frameworks

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

$147K-$272K From the posting source checked Jun 20, 2026

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

87% 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.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Equity

Offered Verified - SEC 10-K source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Machine Learning Engineer - AI & ML Evaluation Frameworks Cupertino, United States of America The Health Sensing Machine Learning Interpretability & Analytics (MLIA) team ensures clinical rigor and contextual trust are at the foundation of Apple's health sensing features. We are looking for an exceptional ML Engineer to help us build the next generation of scalable evaluation infrastructure and lead rigorous investigations into model performance. You will develop cutting-edge tools, synthetic data pipelines, and automated frameworks that ensure our health features are mathematically sound, demographically equitable, and clinically safe. If you are passionate about AI safety, robust software architecture, and pushing the boundaries of ML innovation, come join us! In this role, you will architect and build large-scale evaluation frameworks to interrogate unimodal ML systems and multi-modal foundation models. Beyond infrastructure, you will lead deep-dive ML evaluations, performing failure analysis to uncover performance gaps, reasoning flaws, and edge cases. You will translate findings into actionable insights and work directly with algorithm teams to improve the safety and reliability of our health features. Your work will empower teams across Apple to rapidly evaluate multi-modal sensor fusion while upholding Apple's privacy standards. Design robust methodologies and scalable frameworks to assess the performance, reliability, and safety of both traditional ML and foundation models (e.g., LLMs, diffusion models). Drive failure analysis along with building instrumentation to detect clinical hallucinations, reasoning flaws, and edge cases. Expand LLM/diffusion-based data generation pipelines that enable model training and evaluation without exposing real user data. Build data adaptors and

Read the full description at jobs.apple.com. FewerJobs shows a preview and links to the original posting.