AIML - Applied ML Engineer, Data Generation & Verification

Apple•Seattle, WA

30d

About The Position

This role focuses on enhancing the annotation quality capabilities of Apple's central data annotation platform. You'll work on a cloud-based system that supports machine learning model development across all Apple products. The position combines backend engineering with research and concept development to improve how we measure, monitor, and optimize annotation and ML dataset quality at scale. You'll be responsible for building systems that ensure high-quality training data while developing innovative approaches to quality assessment and feedback mechanisms. As part of a close-knit team of half a dozen engineers focused on annotation quality initiatives and embedded in an even larger team of fellow data annotation engineers, you'll collaborate on shared challenges and contribute to collective problem-solving efforts. The team works together on overlapping projects, combining individual expertise in backend and frontend engineering, statistics, and ML to advance the state of annotation quality measurement and optimization. You'll have the opportunity to learn from peers tackling similar technical problems while contributing your own insights to push the boundaries of what's possible in large-scale data quality assessment. Your contributions will directly impact the foundation of Apple's AI and machine learning capabilities, ensuring that models across the entire product ecosystem are trained on the highest quality data possible.

Requirements

PhD in Computer Science, Machine Learning, Statistics, Math, Physics or related field, or 5 years of equivalent work experience
3+ years of backend engineering experience with cloud-based services
Proficiency in Python and/or Golang for backend development
Experience with AWS services including Lambda and RDS/Aurora
Strong foundation in applied statistics and machine learning fundamentals

Nice To Haves

5+ years of experience in data annotation, data quality, or related ML value chain roles
Experience building and scaling web-based platforms for machine learning workflows
Advanced knowledge of statistical methods for data quality assessment
Experience with distributed systems and microservices architecture
Background in annotation workflow optimization
Track record of research publications or patents in data quality or machine learning
Experience working in cross-functional teams with both technical and non-technical stakeholders

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