ML Model Quality Engineer — Data Integrity for GenAI

Apple, Inc.; Austin (TX); On-site; USD 140,000 - 210,000; Full time

Job summary

Apple is seeking a Machine Learning Engineer focused on Model Quality and data integrity across Wallet, Payments, and Commerce. You will build validation frameworks, observability metrics, and telemetry analyses to ensure that every model is trained, evaluated, and deployed on trustworthy data.

You will work with Data Engineering, Privacy, and Legal teams to monitor health of training datasets, detect bias, and calibrate generative AI systems to deliver robust financial features at scale.

Qualifications

• Bachelor's degree with hands-on ML/AI model quality or related research, or MS/PhD strongly preferred.
• 3+ years in defining and tracking ML model health metrics in production.
• 3+ years in ML, data science, or related analytical roles focusing on data quality or observability.

Responsibilities

• Curate and maintain gold-standard ground-truth datasets for model evaluation and validation.
• Audit training data for bias and fairness; establish checks for data drift.
• Define and analyze telemetry across GenAI workflows and monitor quality signals.
• Design mechanisms to detect degradation of models in production with privacy-preserving methods.
• Define, track, and report data quality metrics for engineering and leadership audiences.
• Develop observability dashboards and reporting workflows for real-time model health.

Skills

Python data science

PyTorch

SQL

ML observability

Data quality

Telemetry analysis

Education

MS/PhD in ML, CS, Data Science, Statistics

Bachelor's degree + hands‑on ML experience

Tools

MLflow

Weights & Biases

LangSmith

Tableau / Superset

Spark / PySpark

Job description

Apple is seeking a Machine Learning Engineer focused on Model Quality and data integrity across Wallet, Payments, and Commerce. You will build validation frameworks, observability metrics, and telemetry analyses to ensure that every model is trained, evaluated, and deployed on trustworthy data.

You will work with Data Engineering, Privacy, and Legal teams to monitor health of training datasets, detect bias, and calibrate generative AI systems to deliver robust financial features at scale.