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Annotation Judge Jobs (NOW HIRING)

UnitedHealthcare

Manager of Clinical Annotation - | , |

$91K - $163K/yr

Manager Of Clinical Annotation At UnitedHealthcare, we're simplifying the health care experience ... Use Sound Judgement * Performance Value: Deliver Quality Results * Drive for Results * Manage Time ...

Q Analysts

Data Annotation Technician

Kirkland, WA · On-site

Data Annotation Technician Join Q Analysts and become part of a world-class organization. Q ... Analyze content and use best judgement for proper answers * Meet daily, weekly and monthly velocity ...

Q ANALYSTS LLC

Data Annotation Technician

Kirkland, WA · On-site +1

Q Analysts is looking for Data Annotation Technicians to support Ground Truth Data Collection ... Analyze content and use best judgement for proper answers * Meet daily, weekly and monthly velocity ...

Q ANALYSTS LLC

Data Annotation Technician

Kirkland, WA · On-site

Q Analysts is looking for Data Annotation Technicians to support Ground Truth Data Collection ... Analyze content and use best judgement for proper answers * Meet daily, weekly and monthly velocity ...

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Annotation Judge information

What is an annotation judge?

An Annotation Judge is a professional who evaluates the quality and accuracy of labeled data, such as text, images, or audio, which has been annotated for use in machine learning and artificial intelligence projects. Their main responsibility is to review, verify, and ensure that the data annotations meet specific guidelines and standards. Annotation Judges play a critical role in improving the reliability of training datasets, which directly impacts the performance of AI systems. They often work closely with data annotators, quality assurance teams, and project managers to maintain high data quality.

What are the key skills and qualifications needed to thrive as an annotation judge, and why are they important?

To thrive as an Annotation Judge, you need strong analytical skills, attention to detail, and subject matter expertise relevant to the data being evaluated, usually supported by a degree in a related field. Familiarity with annotation platforms, data labeling tools, and quality assurance systems is typically required. Excellent communication, impartiality, and critical thinking help you provide clear feedback and maintain high annotation standards. These skills are crucial to ensure data accuracy and consistency, which directly impact the performance of machine learning models.

What are some common challenges faced by annotation judges, and how can they effectively overcome them?

Annotation Judges often face challenges such as maintaining impartiality, handling ambiguous or subjective data, and ensuring high consistency across large volumes of work. To overcome these, it’s essential to follow established guidelines closely, communicate regularly with team members for clarification, and participate in calibration sessions. Staying detail-oriented and seeking feedback can also help maintain accuracy and fairness in their assessments.

What is the difference between Annotation Judge vs Data Annotator?

AspectAnnotation JudgeData Annotator
CredentialsTypically requires basic education, sometimes certification in data labelingUsually requires similar or less formal education, often on-the-job training
Work EnvironmentOffice or remote, working with data labeling platformsOffice or remote, performing data labeling tasks
Industry UsageUsed across AI, machine learning, and data science projectsCommon in AI, machine learning, and data preparation workflows
Search & Comparison IntentOften compared for roles involving data review and quality controlCompared for entry-level data labeling roles

The main difference between an Annotation Judge and a Data Annotator lies in their roles. Annotation Judges typically review and validate annotations made by Data Annotators, ensuring quality and accuracy. Data Annotators perform the initial labeling of data. Both roles are essential in AI data pipelines, with Annotation Judges focusing on quality control and Data Annotators on data preparation.

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Infographic showing various Annotation Judge job openings in the United States as of August 2026, with employment types broken down into 67% Full Time, and 33% Part Time. Highlights an 33% In-person, and 67% Remote job distribution.

Annotation Operations Manager

Redwood City, CA • On-site

DYNA Robotics Inc
See more jobs at DYNA Robotics Incexternal

Full-time

Posted 20 days ago

Job description

Dyna Robotics trains robots to do real world manipulation tasks, and every one of those behaviors is learned from precisely labeled robot episodes. Our Data Annotation team turns raw teleoperation footage into the labels our imitation learning models depend on, so throughput and quality here are a direct input to how fast the company ships.
Dyna Robotics has raised over $140M, backed by top investors including CRV, First Round Capital, Robostrategy, Salesforce Ventures, NVentures, Amazon, Samsung Next, and LG Technology Ventures. Our team brings together engineers and researchers from Google, Meta, Apple, Amazon, Cruise, Aurora, NVIDIA, along with academic roots at Stanford, Berkeley, MIT, UPenn, and beyond. We're positioned to redefine the landscape of robotic automation.
The Role
This role owns the operational layer of that work. You will run the operation from day one, covering process, reporting, and third party relationships, and step into people management progressively, starting with a small pod of labelers and growing into the full team as you prove yourself. Our Annotation Lead keeps the technical judgment calls: model calibration and promotion, ontology and failure taxonomy design, and cost and planning strategy. You are the person who makes the annotation engine run predictably every single day.
What You'll Do
Operations and reporting
  • Own the reporting rhythm. Run daily and weekly throughput reporting across all active datasets, covering episode totals, review stage progress, and ETAs, and distribute scorecards to the labeling team and stakeholders.
  • Keep the pipeline moving. Own the Encord pipeline end to end: create and configure projects, set up SOPs and ontologies, move datasets through annotate, review, and complete stages, handle grade splits, assignments, and resyncs, and keep dataset updates flowing as new footage lands.
  • Measure and unblock. Track and continuously improve operational metrics such as throughput per headcount, per dataset cycle times, and expected versus actual labeling time, and flag bottlenecks before they turn into blockers.
  • Automate the mechanical. Extend and maintain the automation and runbooks behind these tasks, like the scripted daily totals send, so manual toil shrinks over time.

People and team management
  • Start with a pod, then scale up. You will begin by leading a small pod of labelers, owning their day to day assignments and first line supervision. As that proves out, you will transition into managing the full annotation team, about 12 people today and growing, and formalize sub leads as it scales.
  • Handle approvals. Own timecard, payroll hours, and approval workflows for the team.
  • Grow the team. Run hiring for the annotation team end to end, including sourcing, interviewing, onboarding, and ramp.
  • Develop people. Own performance management: regular 1:1s, performance reviews, coaching, and quality feedback loops.

Third party and vendor management
  • Own the relationships. Be the primary point of contact for our annotation platform (Encord) and for external labeling vendors and partners.
  • Coordinate partners. Run external workstreams by provisioning access and invites, standing up projects and SOPs for partners, and managing scope and priorities with them.
  • Manage the handoffs. Own the scale and vendor upload runbook, and make sure data moves cleanly between our systems and external tooling.
  • Absorb the overhead. Represent annotation ops in vendor and external syncs so the rest of the team stays focused.

What You'll Bring
  • 3+ years running operations, program management, or team management, ideally in data annotation, data operations, ML data pipelines, BPO or vendor management, or a comparable high throughput environment.
  • Direct people management experience. You have supervised a team, run hiring, and handled performance and approvals.
  • Strong operational instincts. You build repeatable processes, track the right metrics, and are comfortable owning dashboards, scorecards, and reporting.
  • Comfort with annotation and labeling tooling such as Encord, plus enough technical fluency to work in spreadsheets, basic SQL or scripts, and pipeline tools without hand holding.
  • Vendor and third party management experience. You can be the accountable point of contact and keep external partners on scope and on schedule.
  • A high tolerance for fragmentation. You can hold roughly 20 small threads a week and still keep the operation predictable.
  • Clear, proactive communication. You surface risks early and keep stakeholders aligned.
Bonus points for
  • Exposure to robotics, computer vision, or imitation learning data.
  • Experience automating manual ops workflows with scripts or lightweight tooling.
  • Experience scaling an annotation team through a growth phase and standing up sub leads.

At Dyna Robotics, we build technology for the real world, which requires a team as diverse as the environments our robots inhabit. We are an equal opportunity employer committed to technical rigor and mutual respect.
Don't let a checklist stop you. Data shows that underrepresented groups often only apply if they meet 100% of the criteria. We value problem-solving and grit over keyword matching. If you're passionate about closing the loop between deployed robots and better models, we want to hear from you, even if you don't check every box.

Apply