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About the Role
Contribute to Machine Learning and Generative AI technologies by ensuring the integrity of data powering AI systems at scale. Build and maintain intelligent systems, validation frameworks, and monitoring pipelines to ensure data quality for ML models in production. Your work will be foundational to ML features used by hundreds of millions of users, collaborating with cross-functional teams.
Responsibilities
- Build and maintain intelligent systems, validation frameworks, and monitoring pipelines.
- Ensure every model is trained, evaluated, and deployed on trustworthy data.
- Own the health of training and validation datasets.
- Define and analyze observability metrics to surface actionable product insights.
- Lead telemetry analysis across GenAI workflows.
- Ensure Apple's financial features are built on the highest-quality data.
- Build validation frameworks.
- Define observability metrics.
- Lead telemetry analysis.
- Keep every model trained, evaluated, and monitored on data teams can trust.
Requirements
- 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 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) 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 in defining and tracking ML model health metrics in production — model performance monitoring, feature drift detection, and observability instrumentation.
- Familiarity 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.
Skills
- Machine Learning
- Generative AI
- Data Quality
- Model Evaluation
- ML Observability
- Python
- Pandas
- NumPy
- Scikit-learn
- SQL
- PySpark
- Spark
- Distributed SQL
- Statistical Methods
- Hypothesis Testing
- Distribution Analysis
- Data Drift Detection
- Statistical Process Control
- Model Performance Monitoring
- Feature Drift Detection
- Observability Instrumentation
- GenAI
- LLM
- Communication
Experience Level
- 3+ years of experience
Education Level
- Bachelor's degree
- M.S.
- Ph.D.
About the Company
- We are defining what exceptional data quality looks like for machine learning across Wallet, Payments, and Commerce.
- Your work sits at the foundation of every ML feature that reaches hundreds of millions of users.
- You'll work at the intersection of statistical rigor and production systems, collaborating closely with ML Engineering, 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 to surface actionable product insights, 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.