ML Data Operations Engineer, AI-Assisted Annotation

Apple • San Diego, CA

3d

About The Position

The Gatos team is a cross-discipline production and research data annotation team within Video Engineering. It delivers labeled data that algorithm teams use to build and evaluate features for millions of devices and users around the world. The ML Data Operations Engineer owns annotation projects from the initial request through delivery, including AI-assisted and agent-based workflows in which models perform much of the labeling and people guide, review, and verify results.

The role works with algorithm teams to define success for each data request and translate requirements into task specifications, labeling guidelines, and quality standards. It owns projects end to end, from pilot through production and delivery, and is accountable for data quality, schedules, and usefulness to customers. The engineer designs, implements, and operates AI-assisted labeling workflows, measures their effect on quality, throughput, and cost, establishes acceptance criteria, performs quality checks and audits, analyzes error patterns, and works with guideline owners and labeling teams to address issues. The role also tracks project status and risks, escalates issues with clear requests and root-cause information, collaborates with cross-functional teams and external labeling partners across time zones, and improves team processes by building and sharing AI-enabled tools and workflows.

Requirements

• Bachelor of Science degree.
• At least 3 years of 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 spanning multiple teams, from ambiguous requirements through delivery.
• Hands-on experience with data and technical tools, such as writing basic SQL queries or scripts, analyzing data in spreadsheets, or configuring labeling platforms.

Nice To Haves

• Experience with model-assisted labeling, pseudo-labeling, or agent-based workflows, including determining where human involvement is necessary.
• Experience using 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.
• Ability to work effectively across time zones with internal teams and external partners.
• Ability to define success with customers before work begins and adapt as their needs change.

Responsibilities

• Work with algorithm teams to define success for data requests and translate requirements into task specifications, labeling guidelines, and quality standards.
• Own annotation projects from pilot through production and delivery, with accountability for quality, schedule, and usefulness of the data to customers.
• Design, implement, and operate AI-assisted labeling workflows in which models pre-label, automation handles routine work, and people focus on judgment and verification.
• Measure the impact of AI-assisted workflows 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 root cause.
• Collaborate with data scientists, software and infrastructure engineers, data collection teams, business operations, and external labeling teams across time zones.
• Build and share AI-enabled tools and workflows to improve how the team works.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level