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Remote Data Annotator Jobs (NOW HIRING)

Phonetician

Menlo Park, CA · On-site +1

$40 - $45/hr

Responsibilities Perform narrow and broad phonetic transcriptions of speech data using the ... annotator reliability exercises Required Qualifications Bachelor's degree (or higher) in ...

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Remote Data Annotator information

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$165K

$243.5K

How much do remote data annotator jobs pay per year?

As of Sep 3, 2026, the average yearly pay for remote data annotator in the United States is $165,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,500.00 and $170,000.00 per year, depending on experience, location, and employer.

What is a remote data annotator?

A Remote Data Annotator is a professional who labels, categorizes, or tags data—such as images, text, audio, or video—while working from a remote location. This annotated data is then used to train and improve machine learning models and artificial intelligence systems. Data annotators ensure the quality and accuracy of data, which is crucial for AI applications like self-driving cars, voice recognition, and search engines. The work can vary from simple labeling tasks to more complex categorization, depending on the project requirements.

What are the key skills and qualifications needed to thrive as a remote data annotator?

To thrive as a Remote Data Annotator, you need strong attention to detail, accuracy, and a basic understanding of data labeling concepts, often supported by a high school diploma or equivalent. Familiarity with annotation platforms, data management tools, and sometimes basic coding or spreadsheet software is typically required. Excellent time management, communication, and self-motivation help you consistently meet deadlines and quality standards while working independently. These skills and qualities ensure the precise labeling of data necessary for training reliable AI and machine learning models.

What are some common challenges faced by remote data annotators, and how can they be addressed?

Remote data annotators often encounter challenges such as maintaining focus during repetitive tasks, ensuring annotation accuracy, and communicating effectively with distributed teams. To overcome these, it's helpful to establish a structured work routine, take regular breaks to prevent fatigue, and leverage collaboration tools for clear communication with project managers and peers. Additionally, staying updated with project guidelines and seeking feedback can significantly improve both productivity and annotation quality.

What is the difference between Remote Data Annotator vs Remote Data Labeler?

AspectRemote Data AnnotatorRemote Data Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote, flexible hoursRemote, flexible hours
Industry UsageAI, machine learning, data scienceAI, machine learning, data science
Job FocusAnnotating complex data types (images, videos)Labeling simpler data (images, text)

Remote Data Annotators typically handle complex data annotation tasks like videos and images, requiring more detailed work. Remote Data Labelers focus on simpler labeling tasks, often involving images or text. Both roles are remote, involve similar skills, and are used in AI and machine learning industries, but differ in complexity and scope of data handled.

More about Remote Data Annotator jobs

What cities are hiring for Remote Data Annotator jobs?

Cities with the most Remote Data Annotator job openings:

What are the most commonly searched types of Data Annotator jobs?

The most popular types of Data Annotator jobs are:

What states have the most Remote Data Annotator jobs?

States with the most job openings for Remote Data Annotator jobs include:

Infographic showing various Remote Data Annotator job openings in the United States as of August 2026, with employment types broken down into 33% Full Time, 33% Part Time, and 34% Contract. Highlights an 100% Remote job distribution, with an average salary of $165,018 per year, or $79.3 per hour.

Senior Technical Program Manager, Human-in-the-Loop Operations

Roblox

San Mateo, CA • On-site, Remote

Full-time

Posted 26 days ago


Job description

As Senior Technical Program Manager for the Human-in-the-Loop (HITL) Operations, you will own the end-to-end lifecycle of Roblox's AI data labeling and model evaluation ecosystem. This is a high-leverage, high-ownership role at the intersection of Research, Engineering, and Operations. You will scale our data operations that is handling million of items evaluated annually today with projection of 4x the current volume by 2030, managing a multi-million annual budget and a distributed workforce of 100+ remote contractors - all while driving the platform evolution from manual workflows to AI-assisted, LLM-accelerated operations.

This is a rare opportunity to build the data infrastructure that directly determines the quality of Roblox's AI models across creator tools, content discovery, in-experience AI, and 3D generative content - at the exact moment when human judgment is the critical ingredient for getting these models right.

You Will

  • Lead data programs end-to-end. Own the full lifecycle of labeling and model evaluation workflows across Roblox's AI teams - from translating ambiguous ML requirements into structured annotation tasks, to overseeing contractor execution, quality review, and dataset delivery to model teams.
  • Define and maintain ground truth. Develop and iterate on labeling guidelines, annotation schemas, and quality frameworks tailored to Roblox's unique data types: 3D mesh quality, texture coherence, search relevance, game novelty detection, NPC behavior evaluation, and AI-generated Luau code assessment.
  • Manage a multi-vendor, distributed workforce. Oversee remote contractor teams, managing workforce allocation, productivity SLAs, quality calibration, and budget forecasting serving across multiple teams with Roblox.
  • Drive the shift to AI-assisted workflows. Partner with Engineering to evaluate and implement LLM-based pre-labeling, LLM-as-a-Judge evaluation, and automated QA routing - reducing FTE coordination overhead and scaling throughput proportional to our annual volume growth.
  • Partner cross-functionally across Roblox AI. Work closely with ML Engineers, Data Scientists, and Product Managers to translate model development needs into concrete, executable data programs, and communicate program status and quality trends to senior leadership.
  • Own operational excellence and KPIs. Define and track SLAs across throughput, inter-annotator agreement, cost per label, and data quality metrics. Surface risks, resolve bottlenecks, and drive continuous process improvement.
  • Shape platform and tooling strategy. Contribute to Roblox's next-generation labeling and evaluation platform strategy - including vendor pilots, internal platform improvements, and the transition to a hybrid human-AI collaborative workflow model.

You Have

  • 5+ years of experience in Technical Program Management, Data Operations, or Operations Management within an AI/ML or data-intensive environment.
  • Deep hands-on experience with data labeling, annotation, or model evaluation - including designing annotation schemas, writing labeling guidelines, and managing quality control at scale.
  • Proven experience managing external vendor relationships and distributed contractor workforces, including workforce planning, quality oversight, and budget management.
  • Strong understanding of the ML lifecycle and the role of human-labeled data in training, fine-tuning, and evaluating models.
  • Proficiency in SQL and Python for querying datasets, analyzing label quality, and building operational dashboards. At a minimum, be able to use AI tools to generate queries.
  • Excellent written and verbal communication skills - able to write rigorous technical specifications and present complex data concepts to both ML researchers and business stakeholders.
  • Proven ability to drive cross-functional alignment and exercise strong judgment, knowing when to champion collaborative efforts versus leading independently.
  • Exceptional critical thinking skills with a demonstrated capacity to navigate ambiguity and execute effectively in 0-to-1 environments without structured guidance.
  • Solid grasp of software development life cycles and hands-on experience leveraging AI-assisted tools like Cursor, Claude, or Gemini to accelerate workflows.

Nice to Have

  • Experience with LLM-based labeling pipelines, LLM-as-a-Judge evaluation, or human-AI calibration loops.
  • Familiarity with labeling platforms such as Label Studio, Scale AI, or Snorkel AI.
  • Experience evaluating 3D, games, videos, or multimodal data beyond standard text and image annotation.
  • Background in vendor platform evaluation or build vs. buy analysis.
  • Experience with data operations at a consumer platform operating at massive scale (100M+ users).
  • Knowledge of Roblox platform and its games, or general gaming experience.