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Data Annotation For Ai Jobs (NOW HIRING)

Bot Auto

Data Annotation Specialist

Houston, TX

... AI" and the foundation of our autonomous driving system. In this role, you will be responsible for ... Prior experience in data annotation for autonomous driving, robotics, or computer vision.

Sunday

Data Annotation Lead

Redwood City, CA · On-site

... for 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 ...

Innodata Inc.

$55 - $60/hr

We provide a range of transferable solutions, platforms, and services for Generative AI / AI ... As the Data/Annotation Engineer, you'll be hands-on with the data itself. You'll administer the ...

Sunday Inc

Data Annotation Lead

Redwood City, CA · On-site

Technical skills to build tools for data annotation * Ability to leverage AI to help improve productivity At Sunday Robotics, we're building technology shaped by real people - curious, creative, and ...

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Data Annotation For Ai information

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

AspectData Annotation For AiData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or on-site, tech companies, AI projectsRemote or on-site, data processing companies
Industry UsageArtificial Intelligence, Machine LearningData management, content moderation
Job FocusPreparing data for AI algorithms through annotationLabeling data for various purposes, including AI

Data Annotation For Ai involves preparing datasets specifically for training AI models, focusing on detailed annotations. Data Labeler is a broader role that includes labeling data for multiple purposes, including AI but also other data management tasks. While both roles require similar skills, Data Annotation For Ai is more specialized towards AI development projects.

What is data annotation for AI?

Data annotation for AI is the process of labeling or tagging data—such as text, images, audio, or video—to make it understandable for machine learning models. Annotators add relevant information to raw data, helping AI systems learn to recognize patterns and make accurate predictions. This step is crucial for training, validating, and testing AI algorithms, especially in tasks like computer vision and natural language processing. High-quality data annotation directly impacts the effectiveness and reliability of AI applications.

What are the key skills and qualifications needed to thrive as a Data Annotation Specialist for AI, and why are they important?

To thrive as a Data Annotation Specialist for AI, you need a keen eye for detail, a solid understanding of data labeling concepts, and often a background in the relevant domain (such as language, images, or audio). Proficiency with annotation platforms, data management systems, and basic familiarity with tools like Excel or Python can be highly valuable. Strong communication, consistency, and time management skills help ensure accuracy and meet project deadlines. These abilities are crucial because high-quality, well-annotated data is foundational for training reliable and effective AI models.

What are some common challenges faced by data annotators working on AI projects, and how can they be addressed?

Data annotators for AI often encounter challenges such as maintaining consistency across large datasets, understanding ambiguous labeling instructions, and managing repetitive tasks. To address these issues, it's important to actively seek clarification on guidelines, participate in team discussions to align on labeling standards, and use annotation tools that flag inconsistencies. Regular feedback sessions with project leads also help improve accuracy and efficiency, fostering a collaborative and supportive work environment.
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Infographic showing various Data Annotation For Ai 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 Specialist

Bot Auto

Houston, TX

Other

Re-posted 10 days ago

Job description

Company Introduction

At Bot Auto, we are revolutionizing the transportation of goods with our cutting-edge autonomous trucks, enhancing the quality of life for communities around the globe. With the agility of a startup and the wisdom of seasoned experts, our team has achieved numerous world-firsts and unparalleled innovations. United by a shared vision, we create groundbreaking solutions that propel the future of transportation. Join us and transform your ideas into reality.

Role Overview

We are seeking a highly meticulous and motivated Data Annotation Specialist to join our team. High-quality data is the lifeblood of our "Physical AI" and the foundation of our autonomous driving system. In this role, you will be responsible for creating, refining, and validating the ground-truth data that powers our perception and mapping stacks. You will work directly with our engineering teams to ensure our models are trained on high-fidelity, ground-truth data that meets our rigorous safety standards.

Key Responsibilities
  • 3D Perception Annotations: Perform high-precision 3D instance labeling, semantic segmentation, and bounding box annotation on multi-sensor data (LiDAR, Camera, Radar, etc.).
  • Vectorized Map Annotation: Annotate and edit high-definition vectorized map elements, including lane geometries, traffic signals, and regulatory features.
  • Human-in-the-Loop Refinement: Examine and refine autolabeling results, identifying edge cases where automated systems may falter.
  • Quality Assurance: Review auto-generated labels against strict pass/fail criteria to ensure only the highest quality data enters our training pipelines.
  • Cross-Functional Feedback: Collaborate closely with Machine Learning and Mapping engineers to provide feedback on labeling guidelines and tool improvements.
  • Documentation: Assist in maintaining clear and concise labeling SOPs (Standard Operating Procedures) to ensure consistency across the data operations team.
Required Qualifications
  • Extreme Attention to Detail: A proven track record of identifying small discrepancies in complex datasets or visual environments.
  • Communication Skills: Outstanding verbal and written communication abilities; ability to clearly explain complex visual scenarios to technical teams.
  • Technical Aptitude: Comfortable working with proprietary software tools and navigating 3D environments (Point Clouds/Bird's Eye View).
  • Adaptability: Ability to thrive in a fast-paced startup environment and pivot between perception and mapping tasks as project priorities shift.
  • Professionalism: High degree of self-discipline and the ability to work independently while meeting rigorous quality and throughput targets.
Preferred Qualifications
  • Prior experience in data annotation for autonomous driving, robotics, or computer vision.
  • Understanding of autonomous vehicle sensor modalities (LiDAR, Radar, Cameras).
  • Experience with 3D labeling tools.
  • Familiarity with HD maps.
Additional Information
  • Onsite Requirement: This position requires being onsite at our Houston, TX 5 days per week.
  • Benefits: Comprehensive benefits with the opportunity to work at the forefront of the autonomous trucking industry.

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