Machine Learning Engineer, AI/ML Model Quality

Would you like to contribute to Machine Learning and Generative AI technologies? Are you passionate about the integrity of the data that powers AI systems at scale? Do you believe that trustworthy data is the foundation of every great model? We truly believe it is!

We are defining what exceptional data quality looks like for machine learning across Wallet, Payments, and Commerce. As a Machine Learning Engineer, AI/ML Model Quality, you will build and maintain intelligent systems, validation frameworks, synthetic data generation models and monitoring pipelines that keep our data ecosystem healthy — ensuring that every model we build is trained, evaluated, and deployed on data we can trust. Your work sits at the foundation of every ML feature that reaches hundreds of millions of users.

You'll work at the intersection of machine learning, statistical rigor and production systems, collaborating closely with Data Engineering, Privacy, and Legal teams. This unique opportunity puts you at the center of ML and AI quality — owning the health of training and validation datasets, defining and analyzing observability metrics, building models to calibrate & measure robustness, and leading telemetry analysis across GenAI workflows — ensuring Apple's financial features are built on the highest-quality data, whether powering conventional ML models or the latest generative AI systems.

Description

The ideal candidate is detail-obsessed who understands that model quality starts long before training — it starts with the data. You have strong statistical instincts, know how silent degradation and data drift manifest in production systems, and can translate raw quality signals into insights that drive real decisions.

You will own the health of the data ecosystem that underpins ML and GenAI features across Wallet, Payments, and Commerce — building validation frameworks, defining observability metrics, and leading telemetry analysis that keeps every model trained, evaluated, and monitored on data teams can trust. Your work sits at the foundation of every ML feature that reaches hundreds of millions of users.

Minimum Qualifications

A Bachelor's degree with exceptional hands-on experience in ML/AI model quality or applied research or a M.S or Ph.D in Machine Learning, Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field is strongly preferred.

3+ years of experience in defining and tracking ML model health metrics in production — model performance monitoring, feature drift detection, and observability instrumentation.

3+ years of experience in Machine Learning, data science or a closely related analytical role, with a strong focus on data quality, model evaluation, or ML observability in production environments.

Proficiency in Python (Pandas, NumPy, Scikit-learn), common ML frameworks like PyTorch and SQL for complex data analysis, metric creation, and validation.

Experience querying and analyzing large-scale datasets using distributed computing frameworks (e.g., PySpark, Spark, or distributed SQL).

Solid understanding of statistical methods — hypothesis testing, distribution analysis, data drift detection, and statistical process control.

Experience with GenAI or LLM systems, including common quality failure modes, output evaluation approaches, and telemetry instrumentation.

Strong communication skills — ability to translate complex data quality findings and model health risks into clear, actionable insights for both engineering and non-technical stakeholders.

Preferred Qualifications

Experience with data visualization and dashboarding tools (e.g., Tableau, Apache Superset, Databricks) to present complex ML telemetry.

Familiarity with LLM evaluation frameworks (e.g. LangSmith) or techniques like LLM-as-a-judge.

Experience with Bayesian or causal graph-based approaches to synthetic data generation.

Familiarity with confidence calibration techniques and uncertainty quantification.

Experience with ML monitoring or observability platforms (e.g., MLflow, Weights & Biases, or equivalent).

Experience working with privacy-constrained data or under regulatory compliance frameworks (GDPR, DMA).

Background in financial services, fintech, or consumer payment products.