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Data Annotation Services Jobs (NOW HIRING)

Expert Technology Services

Data Annotator

Irving, TX · On-site

$109K - $132K/yr

Support the development of autonomous mining machines by executing manual data annotation, sensor data fusion, and rigorous QA review, creating high-quality datasets for advanced AI models ...

Expert Technology Services

Data Annotator

Irving, TX · On-site

$109K - $132K/yr

Data Annotator & QA Reviewer - Autonomy & Robotics (Mining) - Perform manual data annotation and quality assurance (QA) review for perception and VLA (Vision-Language-Action) data, including video ...

Expert Technology Services

Data Annotator

Irving, TX · On-site

$109K - $132K/yr

Data Annotator & QA Reviewer - Autonomy & Robotics (Mining) Main Responsibilities: - Execute manual data annotation and rigorous QA review for perception (video, images, sensor data) and VLA (Vision ...

Expert Technology Services

Data Annotator

Irving, TX · On-site

$109K - $132K/yr

Perception annotation: video, images, machine sensor data that describe objects, 3D, trajectory, etc VLA annotation: task, action, intent, chain of causation, outcomes Build the brain behind the ...

Expert Technology Services

Data Annotator

Irving, TX · On-site

Here's a job summary in list format based on your description for the Data Annotator & QA Reviewer (Autonomy & Robotics - Mining Operations): --- ### Job Summary - Perform Manual Data Annotation & QA ...

Expert Technology Services

Data Annotator

Irving, TX · On-site

Data Annotator & QA Reviewer - Autonomy & Robotics (Mining) Key Responsibilities: - Execute manual data annotation and QA review for perception (video, images, 3D sensor data) and Vision-Language ...

JPMorganChase

Data Domain Architect Lead

Wilmington, DE · On-site

JPMorganChase is a leading financial services firm, helping nearly half of America's households and ... annotation, curation, and validation while collaborating with other teams to optimize training data ...

J.P. Morgan

Data Domain Architect Lead

Columbus, OH

Customers expect tailored servicing and Chase is looking to deliver personalization to meet their needs. This is powered by high-quality annotated data and detailed annotation schemes that are the ...

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Data Annotation Services information

What are the key skills and qualifications needed to thrive in data annotation services?

To excel in Data Annotation Services, strong attention to detail, data literacy, and a foundational understanding of data labeling processes are essential, often requiring a high school diploma or equivalent. Familiarity with annotation platforms, labeling tools, and sometimes basic knowledge of scripting or data management systems is typically expected. Strong work ethic, consistency, and effective communication skills help individuals stand out in collaborative, deadline-driven environments. These capabilities ensure high-quality, accurate labeled data, which is critical for training reliable machine learning models.

What is the difference between Data Annotation Services vs Data Labeling Specialists?

AspectData Annotation ServicesData Labeling Specialists
CredentialsTypically no formal credentials required; focus on trainingOften have training in specific tools or industry standards
Work EnvironmentCollaborative, often remote or in-office teamsSimilar, working in teams or independently on labeling tasks
Industry UsageUsed by AI/ML companies for training datasetsEmployed in similar settings, focusing on labeling data for AI models
Search & Comparison IntentUnderstanding services offered for data preparationLooking for roles or tasks related to data labeling

Data Annotation Services encompass the broader process of preparing and annotating data for AI and machine learning projects, often provided by specialized companies. Data Labeling Specialists are individual professionals or team members who perform the actual labeling tasks within these services. While both are closely related, services refer to the overall offering, whereas specialists are the personnel executing the work.

What are some common challenges faced when working in data annotation services, and how can I address them?

In data annotation services, one common challenge is maintaining consistency and accuracy, especially when handling large datasets or ambiguous data points. Clear annotation guidelines and regular communication with team leads help ensure that everyone interprets the data similarly. Additionally, repetitive tasks can lead to fatigue, so it's important to take scheduled breaks and leverage available annotation tools to streamline workflows. Collaborating with peers to discuss edge cases also helps improve overall data quality and fosters a supportive team environment.

What are data annotation services?

Data annotation services involve labeling or tagging data—such as images, text, audio, or video—to make it understandable for machine learning models. These services are essential in training artificial intelligence systems to recognize patterns, objects, or other relevant information in raw data. Companies use data annotation to improve the accuracy and effectiveness of AI applications, such as self-driving cars, chatbots, and image recognition. Professional annotators or specialized platforms often perform these tasks to ensure high-quality, consistent results.
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Data Annotation Services jobs near you
Infographic showing various Data Annotation Services job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 16% Part Time, 1% Temporary, and 4% Contract. Highlights an 94% Physical, 2% Hybrid, and 4% Remote job distribution.

Data Annotator

Expert Technology Services

Irving, TX • On-site

$109K - $132K/yr

Contractor

Posted 6 days ago

Job description

Job Summary (List Format): Data Annotator & QA Reviewer (Autonomy & Robotics)
Main Purpose:
Support the development of autonomous mining machines by executing manual data annotation, sensor data fusion, and rigorous QA review, creating high-quality datasets for advanced AI models.
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### Key Responsibilities:
- Perception & Sensor Data Annotation
- Annotate objects and features in video, images, and 3D sensor data (LiDAR, radar) related to mining environments.
- Precisely label spatial entities (haul roads, rock piles, machinery, vehicles, personnel) in both 2D and 3D data.
- Track and annotate the motion and orientation of heavy equipment in operational scenarios.
- Align and cross-reference multi-sensor data (visual, GPS/GNSS, IMU, CAN bus, hydraulic sensors) for accurate mapping.
- Vision-Language-Action (VLA) Annotation
- Decompose and label complex mining tasks into detailed actions, intents, and outcomes.
- Annotate machine/operator intent (e.g., maneuver decisions, speed adjustments) and chain of causation in operational sequences.
- Tag environmental triggers and verify expected vs. actual outcomes of mining activities.
- Quality Assurance (QA) & Audit
- Conduct high-precision QA audits of annotated datasets, focusing on edge cases (e.g., dust, poor lighting, complex terrain).
- Ensure temporal consistency and correct semantic labeling throughout data sequences.
- Provide feedback and help update labeling guidelines as new mining scenarios arise.
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### Required Skills & Qualifications:
- 1+ years of experience in data annotation, labeling, or QA for computer vision, robotics, or autonomous systems.
- Experience working with 3D spatial data (LiDAR, depth maps, multi-camera feeds).
- Familiarity with standard labeling software (e.g., CVAT, Labelbox, Scale AI, Supervisely, V7, Encord).
- Ability to analyze and structure complex heavy machinery interactions into clear task-action-intent-outcome flows.
- Comfortable handling geospatial and structured data formats (JSON, XML).
---
### Key Technical & Soft Skills:
- Understanding of mining operations, heavy equipment, and pit safety.
- Strong 3D spatial visualization and attention to detail, especially in challenging visibility conditions.
- Effective communication for providing feedback and updating annotation guidelines.
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### Desired/Preferred Experience (Nice to Have):
- Background in Mining Engineering, Geotechnical Engineering, Robotics, Autonomous Vehicles, or related fields.
- Experience with autonomous haulage systems, telemetry logs, or industrial robotics VLA models.