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Remote Text Annotation Jobs in San Jose, CA (NOW HIRING)

Remote Text Annotation information

See San Jose, CA salary details

$18

$32

$44

How much do remote text annotation jobs pay per hour?

As of Aug 24, 2026, the average hourly pay for remote text annotation in San Jose, CA is $32.43, according to ZipRecruiter salary data. Most workers in this role earn between $25.34 and $38.89 per hour, depending on experience, location, and employer.

What is remote text annotation?

Remote text annotation is the process of labeling or tagging text data from a remote location, typically as part of preparing data for machine learning and artificial intelligence applications. This work involves identifying and marking elements such as entities, keywords, sentiment, or other features within text documents. Remote text annotators work from home or another off-site location using specialized software tools. Their annotations help improve AI models by providing accurately labeled datasets. This role is essential in industries like natural language processing, search engines, and chatbots.

What are the key skills and qualifications needed to thrive as a remote text annotation specialist?

To thrive as a Remote Text Annotation Specialist, you need strong attention to detail, excellent reading comprehension, and a solid understanding of language concepts, often supported by a background in linguistics or related fields. Familiarity with annotation platforms, data labeling tools, and sometimes knowledge of scripting languages like Python are typical technical requirements. Strong time management, adaptability, and effective communication are standout soft skills for remote collaboration and meeting project deadlines. These abilities are crucial for ensuring high-quality, accurate data annotations that power machine learning and AI applications.

What are some common challenges encountered in a remote text annotation role, and how can they be effectively managed?

Remote text annotation professionals often face challenges such as ambiguous data, maintaining consistency in labeling, and managing communication with geographically dispersed teams. To address these, it's important to refer to detailed annotation guidelines, attend regular virtual check-ins, and use collaborative tools for feedback and clarification. Staying organized and proactive in raising questions about unclear cases helps ensure high-quality annotations and smooth workflow.

What is the difference between Remote Text Annotation vs Remote Data Labeling?

AspectRemote Text AnnotationRemote Data Labeling
Primary FocusAnnotating text data for NLP modelsLabeling various data types, including images, audio, and text
Skills RequiredLanguage understanding, attention to detail, basic tech skillsData understanding, technical skills, multi-modal data handling
Work EnvironmentRemote, flexible hours, online platformsRemote, flexible hours, online platforms
Industry UsageNatural Language Processing, AI developmentMachine learning, AI, data science

Remote Text Annotation and Remote Data Labeling share similarities in remote work environment and industry usage. However, Remote Text Annotation specifically involves annotating textual data for NLP models, while Remote Data Labeling encompasses labeling various data types, including images and audio. Both roles require attention to detail and technical skills, but Remote Data Labeling often demands broader data handling expertise.

What are popular job titles related to Remote Text Annotation jobs in San Jose, CA?

For Remote Text Annotation jobs in San Jose, CA, the most frequently searched job titles are:

What job categories do people searching Remote Text Annotation jobs in San Jose, CA look for?

The top searched job categories for Remote Text Annotation jobs in San Jose, CA are:

What cities near San Jose, CA are hiring for Remote Text Annotation jobs?

Cities near San Jose, CA with the most Remote Text Annotation job openings:

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

Roblox

San Mateo, CA • On-site, Remote

Full-time

Posted 17 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.