ML Data Operations Engineer, AI-Assisted Annotation

Apple • San Diego, CA

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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. The ML Data Operations Engineer owns annotation projects from the initial request through delivery, including a growing set of AI-assisted and agent-based workflows in which models perform much of the labeling while people guide, review, and verify results.

The role works with algorithm teams to define success for data requests and translate requirements into task specifications, labeling guidelines, and quality standards. It owns projects from pilot through production and delivery, with accountability for quality, schedule, and usefulness to customers. It also designs and operates AI-assisted labeling workflows, measures their impact on quality, throughput, and cost, and improves team tools and processes using AI.

Requirements

• Bachelor of Science degree.
• At least 3 years of relevant industry experience.
• Experience delivering data for machine learning, such as through data annotation, data operations, or technical program management.
• Experience defining annotation tasks, labeling guidelines, and quality metrics.
• Experience owning projects across 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.
• Experience with model-assisted labeling, pseudo-labeling, or agent-based workflows, including assessing where people need to remain in the loop.
• Experience with Python or SQL for data analysis and automation.
• 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.

Nice To Haves

• Familiarity with computer vision data such as images, video, depth, or 3D.
• Experience building reports or dashboards for cross-functional partners.

Responsibilities

• Work with algorithm teams to define success for each data request and turn requirements into task specifications, labeling guidelines, and quality standards.
• Own annotation projects from pilot through production and delivery, accountable for data quality, schedule, and usefulness to the customer.
• Design, implement, and operate AI-assisted labeling workflows in which models pre-label, automated steps handle routine work, and people focus on judgment and verification.
• Measure the impact of labeling workflows on quality, throughput, and cost.
• Set acceptance criteria, conduct quality checks and audits, analyze error patterns, and coordinate follow-up with guideline owners and labeling teams.
• Track project status and risks, raise issues early, and communicate clear requests and root causes.
• 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