ML Data Operations Engineer, AI-Assisted Annotation
Apple San Diego, California, United States
Computers and Electronics Manufacturing · 10,001+ employees
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About the role
The operations engineer will own end-to-end annotation projects, from defining task specifications and quality bars to delivery. They will design and implement AI-assisted labeling workflows while collaborating with cross-functional teams to measure and improve data quality and throughput.
What they look for
Requirements
Candidates must have a Bachelor's degree and at least 3 years of relevant industry experience in data operations or machine learning data delivery. Proficiency in technical tools such as SQL or Python and experience in defining labeling guidelines and quality metrics are required.
Full description
The Gatos team is a cross-discipline production and research data annotation team within Video Engineering. We deliver the labeled data that algorithm teams use to build and evaluate features for millions of devices and users around the world. We're looking for an operations engineer who will own annotation projects from the first request to delivered data. That includes a growing set of AI-assisted and agent-based workflows, where models do much of the labeling and people guide, review and verify the results.
Description
In this role you will:
Work with algorithm teams to define what success looks like for each data request, and translate it into a clear task specification, labeling guidelines and a quality bar. Own annotation projects end to end, from pilot through production to delivery, and be accountable for quality, schedule and the usefulness of the data to the customer. Design, implement, and run AI-assisted labeling workflows: models pre-label, automated steps handle routine work, and people focus on judgment and verification. Measure the impact on quality, throughput and cost. Set acceptance criteria, run quality checks and audits, analyze error patterns, and close the loop with guideline owners and labeling teams. Track project status and risks, and raise issues early with a clear ask and a root cause. Collaborate with data scientists, software and infrastructure engineers, data collection teams, business operations, and external labeling teams across time zones. Improve how the team works by building and sharing tools and workflows that use AI.
Minimum Qualifications
BS and a minimum of 3 years relevant industry experience. Experience delivering data for machine learning, such as data annotation, data operations, or technical program management. Experience defining annotation tasks, labeling guidelines, and quality metrics. Experience serving as the accountable owner for projects that span multiple teams, from ambiguous requirements to delivery. Experience working hands-on with data and technical tools, such as writing basic SQL queries or scripts, analyzing data in spreadsheets, or configuring labeling platforms.
Preferred Qualifications
Experience with model-assisted labeling, pseudo-labeling, or agent-based workflows, including judging where people must stay in the loop. Experience with Python or SQL for data analysis and automation. Familiarity with computer vision data such as images, video, depth, or 3D. Experience building reports or dashboards for cross-functional partners. Clear written and verbal communication, including status updates and escalations to senior leaders. Works effectively across time zones with internal teams and external partners. Defines success with customers before starting the work, and adapts as their needs change.
