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On Call Annotation information

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$10

$17

$25

How much do on call annotation jobs pay per hour?

As of Aug 25, 2026, the average hourly pay for on call annotation in the United States is $17.91, according to ZipRecruiter salary data. Most workers in this role earn between $15.38 and $19.23 per hour, depending on experience, location, and employer.

What is an on call annotation job?

On call annotation jobs involve labeling or tagging data—such as images, audio, text, or video—on an as-needed basis, usually for use in machine learning and artificial intelligence projects. Workers are contacted or scheduled to work only when annotation tasks become available, making the job highly flexible but inconsistent in hours. These roles typically require attention to detail and the ability to follow specific guidelines, as accuracy is crucial for training AI systems. On call annotation jobs can be performed remotely, and may involve using specialized annotation tools provided by employers or clients.

What are the typical responsibilities and expectations for someone working in an on call annotation role?

In an On Call Annotation role, you can expect to be responsible for accurately labeling and categorizing data such as images, audio, or text, often to support machine learning projects. As the position is on-call, work assignments may vary in frequency and urgency, requiring flexibility and the ability to quickly adapt to new guidelines or project needs. Attention to detail and consistent communication with project managers or team leads are essential. Collaboration with other annotators is common, especially when clarifying annotation standards or resolving ambiguities, so being a proactive communicator helps ensure project quality and efficiency.

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

To thrive as an On Call Annotation Specialist, you need strong attention to detail, proficiency in data labeling, and familiarity with annotation guidelines, often supported by a background in linguistics, computer science, or a related field. Experience with annotation tools (such as Labelbox, Prodigy, or internal platforms) and the ability to quickly learn proprietary systems are typically required. Excellent time management, adaptability, and clear communication are essential soft skills for handling variable workloads and collaborating with remote teams. These skills ensure accurate, high-quality data annotations that support machine learning projects and meet project deadlines.

What is the difference between On Call Annotation vs Data Labeler?

AspectOn Call AnnotationData Labeler
Required credentialsHigh school diploma or equivalent; some roles may require basic technical skillsHigh school diploma or equivalent; training often provided
Work environmentRemote or on-site; flexible hours, often project-basedPrimarily remote; task-focused, repetitive work
Industry usageUsed in AI/ML development, autonomous vehicles, healthcareUsed in AI/ML training datasets, image/video annotation
Search and comparison intentHigh overlap; both involve data annotation tasks

On Call Annotation and Data Labeler roles both involve annotating data for AI and machine learning projects. On Call Annotation typically offers flexible, project-based work with potential for varied tasks, while Data Labelers focus on labeling datasets, often with repetitive tasks. Both roles are essential in training AI systems and are commonly sought by individuals interested in data annotation careers.

More about On Call Annotation jobs

What are the most commonly searched types of Annotation jobs?

The most popular types of Annotation jobs are:

What job categories do people searching On Call Annotation jobs look for?

The top searched job categories for On Call Annotation jobs are:

On Call Annotation jobs near you
Infographic showing various On Call Annotation job openings in the United States as of August 2026, with employment types broken down into 13% As Needed, 61% Full Time, 13% Temporary, and 13% Contract. Highlights an 100% In-person job distribution, with an average salary of $37,257 per year, or $17.9 per hour.

Network Engineer - Data Annotation

San Francisco, CA • Remote

Mercor
See more jobs at Mercorexternal

$50 - $70/hr

Full-time

Posted 6 days ago

Job description

About the job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Position: Network Engineer - Data for Autonomous Systems annotation
Type: Contract
Compensation: $50–$70/hour
Location: Remote
Commitment: 30–40 hours/week

Role Responsibilities

  • Review real-world data from deployed networks, including logs, configs, telemetry, and event streams.
  • Label and classify network behaviors, issues, anomalies, and incident patterns.
  • Help define schemas and structure for large-scale data pipelines that downstream ML models will train on.
  • Collaborate with client teams to ensure data accuracy and relevance.
  • Work independently and asynchronously to meet deadlines while improving AI model performance.

Qualifications

Must-Have

  • Experience as a Level 3 / Tier 3 / Principal Support Engineer with on-call rotation, RCAs, and final escalations.
  • Hands-on experience with end-customer enterprise networks, including switches, APs, and firewalls.
  • Proficiency in Wi-Fi/wireless technologies, including Cisco WLC, Aruba, Meraki, 802.1X/RADIUS, and wireless troubleshooting.
  • Ability to perform packet-level troubleshooting with Wireshark, tcpdump, and SPAN captures.
  • Authorization to work in the US or Canada without sponsorship.

Compensation & Legal

  • 1099 contract paid to a personal account.

Application Process (Takes 20–30 mins to complete)

  • Upload resume
  • AI interview based on your resume
  • Submit form

Resources & Support

  • For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome
  • For any help or support, reach out to: support@mercor.com

PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.