AIML – Quality Engineering Manager, Data Operations

Summary

Posted:May 05, 2025

Weekly Hours:40

Role Number:200602201

We are seeking a Quality Engineering Manager to lead and guide a team focused on ensuring the quality of data used for training and evaluating machine learning models. This role is critical in shaping the processes, tools, and standards that underpin high-quality training data. It combines data analysis, technical mentorship, and operational execution to strengthen our quality assurance systems and drive impact at scale. The ideal candidate is comfortable navigating complexity, collaborating multi-functionally, and making data-driven decisions that directly influence model performance.

Description

Manage and support a team of Quality Engineers responsible for QA strategy, tooling, and implementation across annotation workflows. Define and refine scalable processes and metrics to assess the quality, consistency, and relevance of labeled data. Partner with Engineering teams to build automated validation logic for detecting inconsistencies and data anomalies. Collaborate with Data Scientists and ML Engineers to analyze how data quality impacts model behavior and identify opportunities for data improvement. Lead multi-functional alignment on annotation QA standards and ensure feedback loops between quality, guidelines, and tooling. Own and evolve golden set evaluations, consensus grading protocols, and annotator quality tracking mechanisms. Conduct root cause analyses on quality issues and drive corrective actions in collaboration with upstream teams. Stay ahead of QA and data quality best practices, and drive continuous improvement in tools and methodologies.

Minimum Qualifications

• 7+ years of experience in data quality, quality engineering, or a related field, including 2+ years in a management or leadership role.
• Extraordinary leadership, communication, and organizational skills.
• Meticulous, strong analytical and problem-solving skills
• Knowledge of data quality concepts, challenges, and best practices
• Proven track record of driving process improvements and managing multi-functional initiatives.

Preferred Qualifications

• Experience with AI/ML/LLM. Familiarity with ML model development cycles and the role of human-labeled data in training and evaluation.
• Experience with large-scale data operations or data-centric ML infrastructure.
• Knowledge of data quality standards, frameworks, and governance best practices.
• Experience designing and implementing automated QA checks and quality monitoring systems.
• Strong data analysis skills; experience with scripting languages (e.g., Python) and data tools (e.g., SQL).

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 $173,100 and $260,400, 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.