This role pays more than
74%
of similar roles. Most pay
$187,850–$246,150
— the shaded band above.
At the midpoint, this role pays about
$242k
versus about
$217k
for comparable roles.
Based on 240 similar postings.
Employer
About Apple Inc
Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
Apple Inc currently has
3587 open roles
on FindRole.
Listed pay typically runs
$165,800–$277,600
across 2768 roles with salary data.
Machine Learning Engineer, ML/GenAI Evaluation joins the Wallet team to establish evaluation criteria, metrics frameworks, and quality standards for models powering financial features. The role involves designing test frameworks, adversarial corpora, and benchmarks to identify failure modes before launch. Key responsibilities include developing methodologies for robustness testing, such as distribution shift and temporal drift, while owning end-to-end fairness evaluations across protected attributes. The engineer will evaluate generative and agentic model outputs using LLM-as-a-judge frameworks, human evaluation protocols, and prompt regression testing to measure hallucination rates and groundedness. Required skills include Python programming, experience with data pipelines, and experiment tracking tools like MLflow or W&B. The role addresses the critical challenge of ensuring accuracy, reliability, and fairness in high-stakes payment and commerce products by translating complex metrics into actionable product quality narratives for cross-functional stakeholders.
What does a Machine Learning Engineer earn in California?
Median $231150 from 200 postings across 30 companies.
Define evaluation criteria and quality metrics for machine learning models powering Wallet features.
Design and maintain structured test sets covering diverse real-world scenarios, including edge cases and adversarial inputs.
Develop methodologies for robustness testing, including distribution shift, out-of-distribution generalization, and temporal drift.
Execute end-to-end fairness evaluations by building bias test suites across protected attributes and user populations.
Evaluate generative model outputs using LLM-as-a-judge frameworks to measure hallucination rates and groundedness.
Own the final quality sign-off process to determine if models meet readiness standards before shipping.
Translate evaluation results into actionable insights to guide model development priorities and product decisions.
What we're looking for
M.S. in Machine Learning, Computer Science, Statistics, Applied Mathematics, or a related technical field (preferred).
PhD in Computer Science, Data Science, Statistics, AI/ML, or a related field (preferred).
Bachelor's degree with 7+ years of experience in ML evaluation, model quality, or applied research.
5+ years of hands-on ML experience with expertise in model evaluation, offline metrics design, and behavioral testing.
Strong programming skills in Python and fluency with evaluation tooling, data pipelines, and experiment tracking.
Proven ability to construct adversarial test suites and evaluate models for distribution shift, out-of-distribution generalization, and temporal drift.
Experience owning model quality sign-off in a cross-functional launch process.
Experience with structured/semi-structured document understanding, OCR, financial data extraction, or causal fairness approaches (preferred).
Machine Learning
Computer Vision
Python
Data Analysis
Statistical Modeling
Deep Learning
Data Pipelines
Visualization
Synthetic Data
Data Augmentation
Model Monitoring
Generative AI
Root-Cause Analysis
Evaluation Frameworks