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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
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As a Machine Learning Engineer, ML/GenAI Evaluation, you will join the team responsible for Wallet, Payments, and Commerce features to establish evaluation criteria, metrics frameworks, and quality standards for machine learning models. You will manage the full evaluation lifecycle by designing test frameworks, adversarial corpora, and benchmarks that account for diverse document formats, languages, and edge cases. Your daily work involves developing methodologies for robustness testing, including distribution shift and temporal drift, while owning end-to-end fairness evaluations to gate model launches. You will evaluate generative and agentic outputs using LLM-as-a-judge frameworks and prompt regression testing. The role requires proficiency in Python and experience with tools like MLflow or W&B. You will solve critical problems regarding model reliability, hallucination rates, and groundedness within the specific domain of financial features and document understanding.
What does a Machine Learning Engineer earn?
Median $230800 from 335 postings across 52 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.
Own the end-to-end fairness evaluation process 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.
Build persona-stratified benchmarks that reflect a global user base across various locales and spending patterns.
Provide final quality sign-off on model readiness before features are launched to the public.
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.
Proven ability to design evaluation frameworks for production systems including precision-recall tradeoffs, calibration, fairness, and task-specific dimensions.
Experience testing for distribution shift, out-of-distribution generalization, and temporal drift in deployed models.
Ability to construct adversarial test suites, aggressor scenarios, and edge-case corpora to identify model failure modes.
Proficiency in Python and experience with evaluation tooling, data pipelines, and experiment tracking.
Experience with structured/semi-structured document understanding, OCR, or financial data extraction (preferred).
Experience with Bayesian/causal approaches to data generation or fairness evaluation (preferred).
Experience evaluating models under privacy constraints or on-device inference settings (preferred).
Familiarity with confidence calibration techniques and uncertainty quantification (preferred).
Background in financial services, fintech, or consumer payment products (preferred).
Machine Learning
Large Language Models (LLMs)
Conversational AI
Python
Generative AI
Natural Language Understanding
Systems Engineering
Scalable Data Solutions