ML Data Operations Engineer, AI-Assisted Annotation

Apple Inc

Confirmed live today High trust

Quick summary

Work type
On-site
Location
San Diego, CA
Salary
$142,300–$263,300 / yr
Posted
2 days ago
Freshness
Confirmed live today

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $208k
This role $203k
$128k $278k
below market most similar roles pay here above market

This role pays less than 58% of similar roles. Most pay $162,000–$254,750 — the blue band above. At the midpoint, this role pays about $203k versus about $208k for comparable roles.

Based on 240 similar postings.

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About Apple Inc

Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software

Apple Inc currently has 3532 open roles on FindRole.

Listed pay typically runs $174,149–$277,600 across 2761 roles with salary data.

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At a glance

TL;DR · ML Data Operations Engineer, AI-Assisted Annotation

The ML Data Operations Engineer, AI-Assisted Annotation joins the Gatos team within Video Engineering to manage production and research data annotation. This role involves owning annotation projects from initial request to final delivery, specifically focusing on AI-assisted and agent-based workflows where models perform labeling while humans provide guidance and verification. You will define success metrics, create labeling guidelines, and design workflows to improve quality, throughput, and cost. Key responsibilities include running quality audits, analyzing error patterns, and collaborating with data scientists and infrastructure engineers. Required skills include experience with data annotation, SQL, and spreadsheet analysis. Preferred qualifications include Python, experience with pseudo-labeling, and familiarity with computer vision data such as images, video, depth, or 3D. You will solve problems regarding data usefulness for algorithm teams.

What you'll do

  • Translate algorithm team requirements into clear task specifications, labeling guidelines, and quality bars.
  • Own annotation projects end-to-end from pilot through production to final data delivery.
  • Design and run AI-assisted labeling workflows using models for pre-labeling and automated steps.
  • Measure the impact of AI workflows on data quality, throughput, and cost.
  • Run quality checks and audits to analyze error patterns and update labeling guidelines.
  • Track project status and risks while providing root cause analysis for issues.
  • Build and share tools and workflows that utilize AI to improve team operations.

What we're looking for

  • BS degree and a minimum of 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 to delivery.
  • Experience working hands-on with data and technical tools, such as writing basic SQL queries, scripts, or configuring labeling platforms.
  • Experience with model-assisted labeling, pseudo-labeling, or agent-based workflows (preferred).
  • Experience with Python or SQL for data analysis and automation (preferred).
  • Familiarity with computer vision data such as images, video, depth, or 3D (preferred).

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