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Freelance Machine Learning Data Annotation Jobs in Austin, TX

Diligent Tec, Inc.

Data Engineer (Machine Learning)

Austin, TX · Remote

$113.50K - $136.30K/yr

Data Engineer (Machine Learning) Location: 100% Remote Duration: Long Term Contract (2+ years) Role Overview The team builds data and machine learning services for advertiser sellers, helping them ...

TalentPros.AI

Staff Machine Learning Engineer

Austin, TX

$120K - $550K/yr

We are looking for visionary Machine Learning Engineers to join our Applied Group, where you'll ... Implement scalable data pipelines, optimize models for performance and accuracy, and ensure they ...

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Freelance Machine Learning Data Annotation information

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$12

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How much do freelance machine learning data annotation jobs pay per hour?

As of May 28, 2026, the average hourly pay for freelance machine learning data annotation in Austin, TX is $21.67, according to ZipRecruiter salary data. Most workers in this role earn between $17.16 and $24.76 per hour, depending on experience, location, and employer.

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 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 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 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 most commonly searched types of Machine Learning Data Annotation jobs in Austin, TX? The most popular types of Machine Learning Data Annotation jobs in Austin, TX are:
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What job categories do people searching Freelance Machine Learning Data Annotation jobs in Austin, TX look for? The top searched job categories for Freelance Machine Learning Data Annotation jobs in Austin, TX are:
What cities near Austin, TX are hiring for Freelance Machine Learning Data Annotation jobs? Cities near Austin, TX with the most Freelance Machine Learning Data Annotation job openings:
Freelance Machine Learning Data Annotation jobs near you

Data Annotation Specialist

Saronic Technologies

Austin, TX • On-site

Contractor

Posted 7 days ago

Job description

Saronic Technologies is a leader in revolutionizing autonomy at sea, dedicated to developing state-of-the-art solutions that enhance maritime operations through autonomous and intelligent platforms.
Job Overview
We are seeking a Data Annotation Specialist to annotate and review visual datasets used to train and evaluate machine-learning models for maritime perception and autonomy. This role supports our software, perception, and autonomy teams by ensuring labeled data is accurate, consistent, and useful for model development.
The ideal candidate has prior computer vision annotation experience, strong visual attention to detail, and the ability to maintain speed and accuracy through repetitive labeling work. This person should be comfortable following detailed instructions, adapting as labeling rules change, and supporting a fast-moving technical team.
This is an on-site, full-time contract role with an intended path to full-time conversion based on performance and business needs. Upon conversion, the employee would be eligible for Saronic's standard full-time benefits. This position reports to the Data Annotation Manager.
Responsibilities
  • Annotate and review large volumes of image, video, infrared, and other sensor data using computer vision labeling methods.
  • Identify vessels, objects, environmental features, and other elements relevant to maritime autonomy.
  • Maintain accuracy, consistency, and productivity across repetitive, detail-heavy datasets.
  • Apply evolving labeling guidelines and escalate unclear edge cases when needed.
  • Perform both manual annotation work and quality review of auto-labeled data as needed.
  • Willingness to support priority project deadlines when needed.
Qualifications
  • Prior experience in computer vision data annotation or labeling.
  • Familiarity with annotation tools such as Labelbox, CVAT, or similar
  • Experience with annotation types such as segmentation masks, bounding boxes, key points, object tracking, or classification.
  • Strong visual pattern recognition, spatial reasoning, and attention to detail.
  • Comfortable performing repetitive, process-driven work for extended periods while maintaining quality.
  • Able to adapt to changing project priorities, labeling rules, and quality standards in a fast-paced environment.
  • Strong communication skills and willingness to ask questions, accept feedback, and collaborate with the team.
  • Basic understanding of maritime environments, autonomous systems, robotics, or defense technology is a plus.

Saronic CCPA Notice for Candidates and California Employees
This role requires access to export-controlled information or items that require "U.S. Person" status. As defined by U.S. law, individuals who are any one of the following are considered to be a "U.S. Person": (1) U.S. citizens, (2) legal permanent residents (a.k.a. green card holders), and (3) certain protected classes of asylees and refugees, as defined in 8 U.S.C. 1324b(a)(3).
Saronic does not discriminate on the basis of race, sex, color, religion, age, national origin, marital status, disability, veteran status, genetic information, sexual orientation, gender identity or any other reason prohibited by law in provision of employment opportunities and benefits.

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