Machine Learning Engineer, AI/ML Model Quality

Build machine learning that earns trust. In this role, you will help design and operate the validation and monitoring systems that keep Apple’s ML and GenAI features healthy across Wallet, Payments, and Commerce. You will define observability metrics, run telemetry analysis for both conventional ML and generative AI workflows, and strengthen data quality so models are trained, evaluated, and deployed on data teams can rely on. The impact is practical: clearer model health signals, more robust evaluations, and a data ecosystem designed to support production reliability.

Location: New York, NY (onsite)
Salary: USD 184,700 - 324,800 per yearly

Responsibilities

• Build and maintain intelligent systems including validation frameworks, synthetic data generation models, and monitoring pipelines that protect the health of the data ecosystem used for ML.
• Ensure every model is trained, evaluated, and deployed using data the team can trust, supported by rigorous validation and monitoring.
• Collaborate closely with Data Engineering, Privacy, and Legal teams.
• Own data ecosystem health for ML and GenAI features across Wallet, Payments, and Commerce, including defining observability metrics and leading telemetry analysis.
• Define and analyze observability metrics for ongoing assessment of model and data health.
• Build models to calibrate and measure robustness.
• Lead telemetry analysis across GenAI workflows.

Requirements

• Bachelor’s degree with exceptional hands-on experience in ML/AI model quality or applied research, or an M.S./Ph.D. in Machine Learning, Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field is strongly preferred.
• 3+ years of experience defining and tracking ML model health metrics in production, including 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), plus SQL and common ML frameworks such as PyTorch for data analysis, metric creation, and validation.
• Experience querying and analyzing large-scale datasets using distributed computing frameworks such as PySpark, Spark, or distributed SQL.
• Solid understanding of statistical methods including 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, with the ability to translate complex model health and data quality findings into clear, actionable insights for both engineering and non-technical stakeholders.

Technologies

Python, Pandas, NumPy, Scikit-learn, PyTorch, SQL, PySpark, Spark, Tableau, Apache Superset, Databricks, LangSmith, MLflow, Weights & Biases

Benefits

• Comprehensive medical and dental coverage
• Retirement benefits
• A range of discounted products and free services
• Reimbursement for certain educational expenses, including tuition
• Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs
• Eligible for discretionary restricted stock unit awards
• Can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan

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