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Permanent Ai Annotation Writing Jobs (NOW HIRING)

Dyna Robotics

Data Annotation Specialist

As a Data Annotation Specialist, you will be pivotal in iterating on our AI system by annotating ... Clear written communication and a collaborative attitude Preferred : • Hands-on experience ...

Innodata Inc.

AI Solutions Engineer

Washington, DC

$75 - $80/hr

Validate AI feature integration end-to-end across storefront, annotation platform, and DataCard write-back during Phase C Must-Have Qualifications: * Bachelor's degree in Computer Science, Machine ...

YO AI Labs

AI Data Scientist - Remote

New York, NY · Remote

Job Title: AI Data Science Domain Expert Job Type: Contractor (Part-Time) Location: Remote Job ... Technical & Report Writing * Content Review & Editing * Data Annotation * Data Interpretation

YO AI Labs

AI Data Scientist - Remote

Manhattan, NY · On-site

$83 - $131/hr

Job Title: AI Data Science Domain Expert Job Type: Contractor (Part-Time) Location: Remote Job ... Technical & Report Writing * Content Review & Editing * Data Annotation * Data Interpretation

Innodata Inc.

$75 - $80/hr

Validate AI feature integration end-to-end across storefront, annotation platform, and DataCard write-back during Phase C Must-Have Qualifications: * Bachelor's degree in Computer Science, Machine ...

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How much do permanent ai annotation writing jobs pay per year?

As of Aug 22, 2026, the average yearly pay for permanent ai annotation writing in the United States is $58,415.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,500.00 and $58,000.00 per year, depending on experience, location, and employer.

What is permanent AI annotation writing?

Permanent AI Annotation Writing jobs involve the ongoing task of labeling and annotating data—such as text, images, audio, or video—so that artificial intelligence systems can learn and improve. Annotators use specialized tools to identify objects, actions, or features in datasets, providing crucial information that helps train AI models. These positions are typically long-term or full-time, offering stability and the opportunity to develop expertise in AI data preparation. Workers in this field often collaborate with data scientists and engineers to ensure high-quality, accurate annotations. The demand for skilled annotators is growing as AI applications expand into more industries.

What are the key skills and qualifications needed to thrive as a permanent AI annotation writer?

To thrive as a Permanent AI Annotation Writer, you need excellent attention to detail, strong language proficiency, and a basic understanding of data labeling principles, often supported by relevant coursework or experience in linguistics or data science. Familiarity with annotation tools like Labelbox or Prodigy, and experience following annotation guidelines or taxonomies, are commonly required. Strong communication, critical thinking, and the ability to work independently are valuable soft skills in this role. These skills ensure high-quality, consistent data labeling, which directly impacts the performance and reliability of AI models.

What are some typical challenges faced by professionals in permanent AI annotation writing roles, and how can they be addressed?

In permanent AI annotation writing roles, professionals often encounter challenges such as maintaining high accuracy and consistency across large volumes of data, adapting to evolving project guidelines, and managing repetitive tasks. To address these challenges, it's important to develop a systematic approach to annotation, regularly review updated instructions, and collaborate closely with quality assurance teams. Utilizing productivity tools and participating in team discussions can also help streamline workflows and reduce errors, ensuring that the annotated data meets the required standards for AI model training.
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Permanent Ai Annotation Writing jobs near you
Infographic showing various Permanent Ai Annotation Writing job openings in the United States as of August 2026, with employment types broken down into 76% Full Time, 21% Part Time, and 3% Contract. Highlights an 64% Physical, 4% Hybrid, and 32% Remote job distribution, with an average salary of $58,415 per year, or $28.1 per hour.

Data Annotation Lead

Physical Intelligence

San Francisco, CA • On-site

Full-time

Re-posted 23 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