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Freelance Machine Learning Data Annotation Jobs in Texas

Bot Auto

Data Annotation Specialist

Houston, TX · On-site

The Data Annotation Specialist will be responsible for creating, refining, and validating ground ... Collaborate closely with Machine Learning and Mapping engineers to provide feedback on labeling ...

Bot Auto

Data Annotation Specialist

Houston, TX

Collaborate closely with Machine Learning and Mapping engineers to provide feedback on labeling ... Prior experience in data annotation for autonomous driving, robotics, or computer vision.

Bot Auto

Data Annotation Specialist

Houston, TX · On-site

Collaborate closely with Machine Learning and Mapping engineers to provide feedback on labeling ... Prior experience in data annotation for autonomous driving, robotics, or computer vision.

RWS TrainAI

Language Data Annotator (US)

Dallas, TX · On-site +1

Freelance Location: Texas, work from home Work Schedule: Part-time - 10+ hours per week. Flexible ... Experience in one or more of the following areas: machine learning tasks, data collection and ...

RWS TrainAI

Language Data Annotator (US)

Dallas, TX · On-site +1

Freelance Location: Texas, work from home Work Schedule: Part-time - 10+ hours per week. Flexible ... Experience in one or more of the following areas: machine learning tasks, data collection and ...

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Freelance Machine Learning Data Annotation information

What is the difference between Freelance Machine Learning Data Annotation vs Data Labeler?

AspectFreelance Machine Learning Data AnnotationData Labeler
CredentialsBasic understanding of annotation tools, sometimes with specialized domain knowledgeTypically no formal credentials required
Work EnvironmentRemote, flexible, project-basedOften remote or in-house, depending on employer
Industry UsageUsed in AI/ML development for training datasetsUsed in data preparation for various industries, including AI
Search/Comparison IntentFocuses on freelance opportunities, project scope, and toolsMore general, often employed by companies for data labeling tasks

Freelance Machine Learning Data Annotation involves independently completing annotation tasks for AI models, often with specialized tools and domain knowledge. Data Labelers typically perform similar tasks but may work as employees or contractors within a company. The main difference lies in the freelance nature and project-based work of data annotation roles.

What are the key skills and qualifications needed to thrive as a Freelance Machine Learning Data Annotation specialist, and why are they important?

To thrive as a Freelance Machine Learning Data Annotation specialist, you need attention to detail, basic knowledge of data labeling concepts, and familiarity with machine learning data types. Experience with annotation tools (such as Labelbox, RectLabel, or CVAT) and understanding of data privacy protocols are commonly required. Strong communication, time management, and the ability to follow complex guidelines are essential soft skills for delivering accurate results. These skills ensure high-quality, consistent data annotation, which is critical for effective machine learning model training and performance.

What is freelance machine learning data annotation?

Freelance machine learning data annotation involves labeling or tagging data—such as images, text, audio, or video—to help train machine learning models. As a freelancer, you work independently or through platforms, completing specific annotation tasks assigned by companies or researchers. This work is essential because high-quality labeled data is required for AI systems to learn and make accurate predictions. Annotators may categorize images, transcribe speech, or highlight relevant information in documents. The flexibility of freelancing allows you to choose projects and work remotely.

What are some common challenges faced by freelance machine learning data annotators, and how can they be managed?

Freelance machine learning data annotators often encounter challenges such as maintaining data accuracy, handling repetitive tasks, and understanding complex annotation guidelines. Staying organized and regularly reviewing project instructions can help ensure consistency and quality in annotations. Additionally, communicating proactively with project managers and utilizing annotation tools efficiently can help manage workload and clarify uncertainties. Building expertise in different data types (text, image, audio) also allows annotators to diversify their projects and reduce monotony.
What are the most commonly searched types of Machine Learning Data Annotation jobs in Texas? The most popular types of Machine Learning Data Annotation jobs in Texas are:
What are popular job titles related to Freelance Machine Learning Data Annotation jobs in Texas? For Freelance Machine Learning Data Annotation jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Freelance Machine Learning Data Annotation jobs in Texas look for? The top searched job categories for Freelance Machine Learning Data Annotation jobs in Texas are:
What cities in Texas are hiring for Freelance Machine Learning Data Annotation jobs? Cities in Texas with the most Freelance Machine Learning Data Annotation job openings:
Freelance Machine Learning Data Annotation jobs near you
Infographic showing various Freelance Machine Learning Data Annotation job openings in Texas as of July 2026, with employment types broken down into 18% Full Time, 8% Part Time, 68% Contract, and 6% Nights. Highlights an 4% Physical, and 96% Remote job distribution.

Data Annotation Specialist

Bot Auto

Houston, TX • On-site

Full-time

Posted 4 days ago

Job description

Job Summary:
Bot Auto is revolutionizing the transportation of goods with autonomous trucks. The Data Annotation Specialist will be responsible for creating, refining, and validating ground-truth data for the company's perception and mapping stacks.
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.
Qualifications:
Required:
• 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:
• 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.
Company:
Transforming American Transportation with Autonomous Trucks Founded in 2023, the company is headquartered in Houston, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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