AIML - Backend Engineer, Data Generation & Verification

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.

As part of Apple’s central data annotation engineering team, you’ll help build the next generation of AI-powered user experiences. Our work provides high-quality datasets and expert feedback to train machine learning models and evaluate their performance across all products and verticals. The team maintains and develops a global distributed system that captures insights from human domain experts and analysts worldwide, operating at a scale that few companies can match. We leverage cutting-edge human-in-the-loop algorithms to create and curate datasets through fine-tuned multi-step annotation workflows and near real-time data quality feedback signals. You’ll tackle complex challenges in ML data quality at unprecedented scale while collaborating closely with AIML engineering teams across the company. From Siri to the Photos app, from iPhone to Vision Pro, you’ll help bring innovative experiences to millions of users worldwide.

Description

The AIML Annotation Platform provides frameworks and services for effective, privacy-focused data annotation across all of Apple. As a member of our backend engineering team, you'll have the opportunity to work with highly skilled engineers to build reliable backend systems, with a particular focus on the platform's quality stack: the services that score annotation work, compute quality metrics, and give our partners a trustworthy, end-to-end view of data quality. You will work most closely with our backend and quality engineers in Germany and partner regularly with our metrics team in the United States.

Responsibilities include: - Model great software engineering practices by producing well-tested, performant, and reliable code across the backend services we maintain. - Push the boundary of world class quality control and assessment flows across mixed agentic and human annotation and evaluation pipelines, designing the systems that orchestrate, score, and verify work as it moves between automated agents and people. - Design, build, and evolve the backend of our quality and scoring systems: measuring the quality of annotation and review work, computing and serving metrics, and making quality signals actionable for our users. - Engage deeply in architecture and code reviews, pair programming, and design discussions, and write clear architecture and design proposals. - Work across team and organizational boundaries so our tools and systems integrate seamlessly, and cooperatively solve complex problems with your peers. - Constantly seek areas of improvement and help the team deepen and broaden its expertise.

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.

Minimum Qualifications

PhD in Computer Science, Machine Learning, Statistics, Math, Physics or related field, or equivalent work experience Proven experience of backend engineering experience with cloud-based services Proficiency in Python and Golang for backend development Experience with AWS services including Lambda and RDS/Aurora

Preferred Qualifications

Strong foundation in applied statistics and machine learning fundamentals Proven 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