Machine Learning Data Engineer

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.

Description

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.

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.

Preferred Qualifications

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.

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

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.

Minimum Qualifications

BS in Computer Science, Data Engineering, Data Science, Mathematics, or a related field; or equivalent industry experience.

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.

Pay & Benefits

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $181,100 and $318,400, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.