Category: Data Science | Machine Learning | Generative AI | Model Quality & Observability
Employment Type: Full-Time
Weekly Hours: 40
Location: Austin, Texas, United States (3 Work Locations Available)
Posted: June 11, 2026
Role Number: 200667346-0157
About the Role
Apple is seeking a Data Scientist specialising in AI/ML Model Quality to join the team responsible for the data integrity of ML and Generative AI systems powering Apple Wallet, Payments, and Commerce. This is a foundational role — because at Apple, exceptional models begin with exceptional data.
You will own the health of the data ecosystem that sits beneath every ML and GenAI feature reaching hundreds of millions of users worldwide. From building intelligent validation frameworks and defining observability metrics to leading telemetry analysis across GenAI workflows, your work ensures that every model Apple builds is trained, evaluated, and deployed on data the entire organisation can trust.
This role sits at the precise intersection of statistical rigour and production systems — uniquely positioned between ML Engineering, Data Engineering, Privacy, and Legal teams.
What You Will Be Doing
Ground-Truth Dataset Curation & Validation You will curate, analyse, and maintain gold-standard ground-truth datasets used for model evaluation and continuous validation across both conventional ML and GenAI systems — ensuring every benchmark Apple’s models are measured against is trustworthy and up-to-date.
Bias Auditing & Fairness Analysis Before any model ships, you will audit training data for systemic bias and fairness gaps. You will then establish ongoing analytical checks that catch bias introduced by data drift over time — keeping Apple’s financial AI features equitable and compliant.
Data Quality Metrics Definition & Reporting You will define, track, and report key data quality metrics — including completeness, accuracy, timeliness, and validity — presenting findings clearly to both engineering teams and senior leadership.
Automated Data Quality Rules & CI/CD Integration Working alongside Data Engineering, you will design and define automated data quality rules and threshold checks, ensuring these are embedded directly into model development pipelines and CI/CD workflows.
ML Observability & Production Monitoring You will define and own ML observability metrics covering model performance, output distributions, training-serving skew, feature drift, and silent degradation — translating raw production signals into actionable guidance for engineering and product teams.
Observability Dashboards & Reporting You will design and build observability dashboards and reporting workflows that give stakeholders a consistent, real-time view of model health across both ML and GenAI systems.
GenAI Telemetry Analysis You will define and analyse telemetry across GenAI workflows — tracking quality signals such as output coherence, latency, task completion rates, and regression patterns — and translate those findings into concrete recommendations for model and data teams.
Failure Mode Identification Through systematic telemetry analysis, you will identify degradation patterns and domain-specific failure modes in GenAI systems before they impact users.
Minimum Qualifications
- Education: Bachelor’s degree with exceptional hands-on ML/AI model quality or applied research experience — or an MS or PhD in Machine Learning, Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field (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 analysing large-scale datasets using distributed computing frameworks such as PySpark, Spark, or distributed SQL
- Solid understanding of statistical methods — hypothesis testing, distribution analysis, data drift detection, and statistical process control
- Demonstrated experience defining and tracking ML model health metrics in production — including 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 — able to translate complex data quality findings and model health risks into clear, actionable insights for both engineering and non-technical stakeholders
Preferred Qualifications
Candidates with the following experience will be strongly competitive:
- Experience with data visualisation and dashboarding tools such as Tableau, Apache Superset, or Databricks to present complex ML telemetry
- Familiarity with LLM evaluation frameworks such as 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 such as MLflow, Weights & Biases, or equivalent
- Experience working with privacy-constrained data or under regulatory compliance frameworks such as GDPR or DMA
- Background in financial services, fintech, or consumer payment products
About Apple
At Apple, data quality is not a checkbox — it is the bedrock of every AI-powered product that reaches users at scale. This team defines what exceptional data quality looks like for machine learning across Wallet, Payments, and Commerce. Apple is an equal opportunity employer committed to inclusion and diversity, and does not discriminate on the basis of race, colour, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or any other legally protected characteristic.
Salary information was not disclosed for this role. Compensation will be competitive and consistent with Apple’s total rewards package, including equity and comprehensive benefits.