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Hourly Remote Data Labeling Jobs in California (NOW HIRING)

Delivery Lead

San Francisco, CA · Remote

$110K - $140K/yr

... and remote workforce marketplaces can't. We own projects end-to-end, from scoping and protocol ... Our work spans RLHF, evals, red-teaming, and custom multimodal data creation, all powered by Label ...

Remote Commitment: 40 hours/week Role Responsibilities * Guide research and engineering teams on ... Prior experience with data annotation , labeling, evaluation, or human feedback collection.

Annotate, label, and validate data across cybersecurity use cases like CVE classification accuracy ... Hourly Opportunity: Transition to hourly compensation for sustained quality. Application Process ...

New

Data Analyst

Foster City, CA · Remote

$85K - $100K/yr

We run these virtual- and private-label marketplaces in one of the nation's largest media networks ... REMOTE QuinStreet is an equal opportunity employer. We do not discriminate on the basis of race ...

Director Master Data Management

Irvine, CA · On-site +1

$171K - $228K/yr

Remote Position Summary: The Director - MDM COE is responsible for establishing and leading the ... labeling). Leadership & Change Management: * Build and lead a cross-functional team of data ...

Director Master Data Management

Irvine, CA · On-site +1

$171K - $228K/yr

Remote Position Summary: The Director - MDM COE is responsible for establishing and leading the ... labeling). Leadership & Change Management: * Build and lead a cross-functional team of data ...

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Hourly Remote Data Labeling information

What are the key skills and qualifications needed to thrive as an Hourly Remote Data Labeler, and why are they important?

To thrive as an Hourly Remote Data Labeler, you need strong attention to detail, basic computer literacy, and the ability to follow specific guidelines, often with a high school diploma or equivalent. Familiarity with data annotation tools and platforms such as Labelbox, Prodigy, or internal company systems is typically required. Reliability, time management, and effective written communication are crucial soft skills for meeting deadlines and maintaining quality in a remote setting. These skills and qualities are important to ensure accurate, consistent data labeling that directly impacts the performance of AI and machine learning models.

What is the difference between Hourly Remote Data Labeling vs Data Annotation Specialist?

AspectHourly Remote Data LabelingData Annotation Specialist
CredentialsBasic computer skills, attention to detailSimilar credentials, often with some industry-specific knowledge
Work EnvironmentRemote, flexible hoursRemote, often project-based or ongoing
Industry UsageCommon in AI/ML developmentUsed across tech, healthcare, automotive sectors
Search IntentLooking for remote data labeling jobsSearching for data annotation roles

Both roles involve labeling or annotating data for machine learning models, often remotely. The main difference lies in terminology and specific industry usage, but they share similar credentials and work environments.

What is hourly remote data labeling?

Hourly remote data labeling is a job where individuals work from home to tag, categorize, or annotate data (such as images, videos, text, or audio) for machine learning and artificial intelligence projects. Workers are typically paid by the hour and use online platforms to complete labeling tasks assigned by companies or research organizations. This work is crucial because AI models need large volumes of accurately labeled data to learn and function properly. The job usually requires attention to detail and may involve following specific guidelines to ensure data quality.

What are some common challenges faced by hourly remote data labeling professionals, and how can they be managed?

Hourly remote data labeling professionals often encounter challenges such as maintaining consistent accuracy, managing repetitive tasks, and staying self-motivated while working independently. To manage these challenges, it's important to set up a dedicated workspace, take regular breaks to reduce fatigue, and follow established labeling guidelines closely. Frequent communication with team leads and participating in quality feedback sessions can also help ensure your work meets project standards and fosters professional growth.
What are the most commonly searched types of Remote Data Labeling jobs in California? The most popular types of Remote Data Labeling jobs in California are:
What are popular job titles related to Hourly Remote Data Labeling jobs in California? For Hourly Remote Data Labeling jobs in California, the most frequently searched job titles are:
What cities in California are hiring for Hourly Remote Data Labeling jobs? Cities in California with the most Hourly Remote Data Labeling job openings:

Delivery Lead

HumanSignal

San Francisco, CA • Remote

$110K - $140K/yr

Full-time

Posted 17 days ago


Job description

About HumanSignal

Real-world data is the competitive edge in AI.

HumanSignal is a human data partner for companies building AI models and products. Our customers ship better AI, faster, because we partner with their researchers from real-world data creation to annotation to delivery.


We design and create datasets from scratch, recruit and manage the domain experts who evaluate model output, and run everything through our own platform, Label Studio, the open-source standard for data labeling and evaluation, used by over 1 million practitioners worldwide.


We specialize in the operationally complex: real-world data collection, multimodal pipelines, and multi-step workflows. Advanced ML and AI teams use our enterprise platform to run their own data factories, and our services team to extend their reach where in-house capacity runs out.


If you want to do work that materially shapes how the next generation of AI products gets built, we'd love to talk.

Level: Manager
Compensation: $110,000 – $140,000
Location: San Francisco, CA

About the Role

HumanSignal specializes in operationally complex, multimodal data collection and annotation — delivering the datasets that frontier AI research requires and remote workforce marketplaces can't. We own projects end-to-end, from scoping and protocol design through final delivery, running on-site and distributed expert workforces across 50+ knowledge domains, 30+ languages, and 75+ countries. Our work spans RLHF, evals, red-teaming, and custom multimodal data creation, all powered by Label Studio Enterprise and built on a foundation of rigorous quality workflows, ethical sourcing, and full data security. This role sits at the operational core of that delivery engine — responsible for ensuring our clients get the highest-quality data on time, every time.

This role is not for everyone. HumanSignal Services operates at the intersection of frontier AI research and large-scale human data delivery — and the work is fast, demanding, and unforgiving of dropped balls. You'll own complex, high-stakes data programs end-to-end, managing expert workforces, navigating shifting customer requirements, and holding quality and delivery timelines simultaneously. There is no playbook handed to you. You will build it, break it, and rebuild it better. If you thrive under pressure, take personal pride in operational excellence, and don't quit when a project gets hard — this is the role for you

What You'll Do

The day-to-day is intense by design. You'll juggle multiple programs at once, each with its own contributors, quality standards, and customer expectations. You'll make hard calls with incomplete information, hold contributors and vendors accountable, and find creative solutions to problems that don't have obvious answers. The expectation is simple: own the outcome, no excuses. High performers here grow faster than anywhere else in the industry. The bar is high — and that's the point.

  • Lead and manage a team of Strategic Project Leads (SPLs) across multiple high-stakes AI data projects simultaneously
  • Own delivery outcomes for your projects: throughput, quality, SLA performance, cost efficiency, and customer satisfaction
  • Drive delivery across custom data pipelines and expert labeling workflows, translating researcher requirements into clear operational plans
  • Collaborate with AI lab researchers and procurement partners to define data strategies, scope programs, and resolve escalations
  • Drive systems-level improvements — standardize playbooks, improve tooling, and build infrastructure that makes the team faster and more reliable at scale
  • Coach and develop SPLs; ensure high-quality AI Trainer experience and strong contributor retention
  • Partner with Product and Engineering to evolve internal tooling, automation, and operational systems
Required Qualifications
  • 5+ years in operationally intensive roles (marketplace, data ops, logistics, or similar)
  • 1+ year of people management with clear impact on team performance
  • Metrics-driven with a systems-thinking approach to operations
  • Experience owning delivery outcomes across multi-stakeholder, high-velocity projects
  • Hands-on operator willing to dive into execution when needed
  • Must be proficient in using LLMs in your every day work, including building scripting logic and working with large datasets with LLM assistance
Preferred Qualifications
  • 1+ year in AI data operations (RLHF, annotation, model evaluation)
  • STEM background or strong technical fluency
  • Python & REACT working knowledge
  • Experience managing distributed contributor workforces at scale
  • Background in management consulting, investment banking, or high-growth startups

HumanSignal is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. HumanSignal does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, genetic information, or any other characteristic protected by applicable federal, state, or local law. We are committed to working with and providing reasonable accommodations to individuals with disabilities.