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Hourly Ai Data Annotation Jobs (NOW HIRING)

Sunday Inc

Data Annotation Lead

Redwood City, CA · On-site

Build a data annotation team * Own annotation operations end-to-end * Manage the people side of ... Ability to leverage AI to help improve productivity At Sunday Robotics, we're building technology ...

Sunday

Data Annotation Lead

Redwood City, CA · On-site

... data annotation • Ability to leverage AI to help improve productivity Company : Sunday is a robotics and artificial intelligence company that develops an autonomous home robot to assist with ...

GE HealthCare

Data Manager - AI Development

$94K - $141K/yr

Track annotation progress, throughput, and quality metrics. * Maintain annotation dashboards to ensure timely delivery aligned with AI development milestones. 4. Data Governance & Compliance Support

Dyna Robotics

Data Annotation Specialist

As a Data Annotation Specialist, you will be pivotal in iterating on our AI system by annotating data on various tasks performed by robots, directly influencing the performance of robotic arms.

Q Analysts

Data Annotation Technician

Kirkland, WA · On-site

Data Annotation Technician Join Q Analysts and become part of a world-class organization. Q ... AI) and machine learning (ML). Q Analysts is headquartered in San Jose, CA with a presence ...

Tiki AI

AI Data Software Engineer

San Francisco, CA · On-site

$134K - $162K/yr

Tiki AI provides end-to-end data annotation and intelligence solutions that transform raw information into high-quality, actionable datasets. Founded in , the company is headquartered in San ...

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Hourly Ai Data Annotation information

What is an Hourly AI Data Annotation job?

An Hourly AI Data Annotation job involves labeling, tagging, or categorizing data—such as images, text, or audio—to help train machine learning models. Annotators follow specific guidelines to ensure that the data is accurately labeled so that AI systems can learn to recognize patterns and make decisions. These jobs are typically paid by the hour and may require attention to detail, consistency, and sometimes familiarity with specialized annotation tools. This work is essential for improving the accuracy and usefulness of artificial intelligence applications.

What are some common challenges faced by hourly AI data annotators, and how can they be managed?

Hourly AI data annotators often encounter challenges such as repetitive tasks, maintaining high accuracy under time constraints, and adapting to evolving project guidelines. To manage these, it's important to take regular breaks to avoid fatigue, stay up to date with training materials, and communicate proactively with team leads if instructions are unclear. Many teams use collaborative tools and regular feedback sessions to support annotators and ensure consistent quality, making teamwork and attention to detail vital for success in this role.

What are the key skills and qualifications needed to thrive as an AI Data Annotator, and why are they important?

To thrive as an AI Data Annotator, you need strong attention to detail, accuracy, and a basic understanding of data labeling concepts, typically supported by a high school diploma or equivalent. Familiarity with annotation tools such as Labelbox or Supervisely, and basic computer proficiency, are often required. Critical thinking, consistency, and effective communication are valuable soft skills in this role. These skills ensure high-quality, reliable data that directly improves the performance of AI and machine learning models.

What is the difference between Hourly Ai Data Annotation vs Data Labeler?

AspectHourly Ai Data AnnotationData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or in-office, flexible hoursRemote or in-office, flexible hours
Industry UsageAI, machine learning, tech companiesAI, machine learning, tech companies
Job FocusAnnotating data for AI training, often with specific instructionsLabeling data to help AI models learn, often similar tasks

Hourly Ai Data Annotation and Data Labeler roles are similar, focusing on preparing data for AI systems. The main difference lies in terminology; 'Hourly Ai Data Annotation' emphasizes the paid hourly aspect and the specific task of annotating data for AI training, while 'Data Labeler' is a broader term used interchangeably in the industry. Both roles require similar skills and are used in the same industry sectors.

More about Hourly Ai Data Annotation jobs
What cities are hiring for Hourly Ai Data Annotation jobs? Cities with the most Hourly Ai Data Annotation job openings:
What are the most commonly searched types of Ai Data Annotation jobs? The most popular types of Ai Data Annotation jobs are:
What states have the most Hourly Ai Data Annotation jobs? States with the most job openings for Hourly Ai Data Annotation jobs include:
What job categories do people searching Hourly Ai Data Annotation jobs look for? The top searched job categories for Hourly Ai Data Annotation jobs are:
Hourly Ai Data Annotation jobs near you
Infographic showing various Hourly Ai Data Annotation job openings in the United States as of July 2026, with employment types broken down into 73% Full Time, 24% Part Time, and 3% Contract. Highlights an 65% Physical, 3% Hybrid, and 32% Remote job distribution.
Data Annotation Lead

Data Annotation Lead

Physical Intelligence

San Francisco, CA • On-site

Full-time

Posted 22 days ago

Job description

Physical Intelligence is bringing general-purpose AI into the physical world. We are a group of engineers, scientists, roboticists, and company builders developing foundation models and learning algorithms to power the robots of today and the physically-actuated devices of the future.
The role
We're looking for a Data Annotation Lead to own annotation operations and scale the team behind it. Annotation is core to how our models improve, and demand is growing fast. You will scale the annotation workforce from 100s to 1,000s while raising the quality bar - designing the org, the training pipeline, the quality system, and the metrics that let it scale efficiently.
You will own the people and the operation: throughput, quality, cost, and delivery across every annotation type.
In this role you will
  • Own annotation operations end-to-end: throughput, quality, cost, and on-time delivery across all annotation types.
  • Scale the annotation workforce from 100s to 1,000s: workforce planning, org design, and the hiring and onboarding funnel.
  • Build and lead a multi-layer management structure; hire, develop, and manage managers and team leads.
  • Scale throughput with autolabeling and model-based annotation: design human-in-the-loop workflows where models pre-label and annotators review, correct, and escalate, so output grows faster than headcount.
  • Stand up the training and certification pipeline that brings new annotators and teams to the quality bar quickly and consistently.
  • Define and continuously raise the quality bar: rubrics, calibration, audit/QA loops, and quality-adjusted productivity.
  • Establish operational metrics and reporting (presence, throughput, acceptance/rejection, rework) and drive week-over-week improvement.
  • Run capacity planning and prioritization against competing demand; allocate teams to the highest-impact work.
  • Manage performance at scale with clear standards, feedback, and a fair improvement/exit process.
  • Partner with product and engineering to define annotation tooling that unlocks throughput and quality.
  • Partner with research and project leads to translate annotation needs into clear instructions, rubrics, and SLAs.
  • Own the in-house vs. vendor mix and manage external partners where used.
  • Own the annotation operating budget and unit economics; improve cost-per-annotation while protecting quality.

What you'll bring
  • 7+ years leading scaled data or annotation operations, including teams in the 100s+.
  • 3+ years as a manager of managers.
  • Track record standing up 0→1 annotation programs.
  • Deep command of annotation best practices, operations, and strategy.
  • Experience integrating autolabeling and model-based annotation into human workflows; building human-in-the-loop pipelines that raise throughput without sacrificing quality.
  • Fluency with operational and quality metrics; data-driven management of large workforces.
  • Strong cross-functional partnership with product, engineering, and research/ML.
  • Clear written and verbal communication; able to set and hold standards across a large, distributed team.
  • Working understanding of ML and why annotation quality drives model performance.

Nice to have
  • Experience in robotics, autonomous vehicles, or frontier-AI data pipelines.
  • Experience managing distributed/global and/or vendor workforces.
  • Built annotation tooling or partnered tightly with a tooling team.
  • Experience training or fine-tuning autolabeling models, or partnering closely with the ML teams that do.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Apply