This role pays more than
88%
of similar roles. Most pay
$187,850–$246,150
— the shaded band above.
At the midpoint, this role pays about
$255k
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.
As a Machine Learning Engineer, ML/GenAI Evaluation, you will join the Wallet team to establish evaluation criteria, metrics frameworks, and quality standards for machine learning models powering financial features. You will manage the full evaluation lifecycle by designing test frameworks, adversarial corpora, and benchmarks that account for diverse document formats and global user populations. Your daily work involves developing methodologies for robustness testing, including distribution shift and temporal drift, while owning end-to-end fairness evaluations to ensure model reliability. You will evaluate generative and agentic outputs using LLM-as-a-judge frameworks and prompt regression testing to assess hallucination rates and groundedness. The role requires proficiency in Python, experience with tools like MLflow or W&B, and expertise in offline metrics design. You will solve critical problems regarding model accuracy and reliability within the wallet, payments, and commerce domains.
What does a Machine Learning Engineer earn in New York?
Median $246150 from 58 postings across 19 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.
Manage end-to-end fairness evaluations by building bias test suites and gating model launches on fairness criteria.
Create user persona-stratified benchmarks to reflect the diversity of a global user population.
Evaluate generative and agentic model outputs for hallucination rates, faithfulness, and groundedness using LLM-as-a-judge frameworks.
Own the final model quality sign-off process to determine if models are ready for production release.
Translate evaluation results into actionable insights to guide 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 proficiency 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 (preferred); Bayesian or causal 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