Machine Learning Data Engineer

Apple•Cupertino, CA

About The Position

Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other's ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It's the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you'll do more than join something — you'll add something. We are seeking a highly experienced and strategic Machine Learning Data Engineer to drive our machine learning data with a strong focus on quality. In this role, you will transform ambiguous data challenges into scalable processes, clear policies, and high-fidelity datasets that power diverse ML use cases, specifically focused on innovative consumer products and user-facing technologies. You will act as the crucial link between technical tools and infrastructure, cross-functional engineering teams, and regulatory compliance (including privacy, legal, and consumer data protection). If your passion is making sense of complex data, designing data evaluation frameworks, and leading initiatives to maximize model ROI through rigorous data quality, we want you on our team.

Requirements

BS in Computer Science, Data Engineering, Data Science, Math, or related fields.
Experience in data analysis, data engineering, and machine learning data operations.
Experience designing data quality control processes, data curation workflows, or Human-in-the-Loop initiatives.
Experience managing or coordinating cross-functional projects spanning multiple technical teams or organizations, leading end-to-end data strategy for ML development lifecycle, including iterating rapidly to drive improvements.
10+ years of experience in data analysis or ML data operations, including identifying trends, generating summary statistics, and drawing insights from quantitative and qualitative data.
Experience operating within global data privacy frameworks (e.g., GDPR, CCPA) and aligning consumer ML data handling with legal compliance and ethical guidelines.
Proven background in leading complex, cross-functional programs focused specifically on ML data quality at scale.
Demonstrated ability to consult with diverse engineering stakeholders to gather requirements, explain complex models, and iterate rapidly to drive improvements.
Excellent written and verbal communication skills, with a specialized ability to distill highly technical analyses to non-technical audiences effectively.
Exceptional problem-solving skills, adaptability, and agility to navigate high ambiguity, learn proprietary tools quickly, and thrive in a fast-paced environment.

Nice To Haves

Experience with prompt engineering, machine learning tools, and fine-tuning Large Language Models (LLMs).

Responsibilities

Drive ML Data Quality & Validation: Lead the continuous quality management of ML datasets, with a specific focus on human-generated data.
Design and execute rigorous dataset validation processes, incorporating real-time feedback loops to immediately identify, flag, and resolve quality issues before they impact model performance.
Translate Policy to Scalable Processes: Develop sophisticated data processes and policies for complex consumer product domains driven by innovative technology.
Convert ambiguous data quality problems and legal/regulatory constraints into precise, scalable workflows and data guidelines for user-facing features and edge cases.
Build Data Evals & Metrics: Design and implement robust data evaluation frameworks.
Identify key data-centric drivers of model performance and define the metrics that rigorously track data quality, consistency, and integrity at the granular level.
Ensure Privacy, Legal, & Regulatory Compliance: Act as a steward of data integrity.
Integrate privacy requirements, legal data quality standards, and consumer protection regulations directly into the data workflows and policies.
Cross-Functional Leadership: Serve as a bridge between technical and non-technical audiences.
Produce compelling analytical write-ups, dashboards, and data visualizations to convey insights, advocate for data strategy, and align engineering stakeholders.