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Freelance Machine Learning Data Annotation Jobs in Dallas, TX

HEB

Data Engineering Manager

Dallas, TX · On-site

You will partner closely with Product Management, Data Science, Machine Learning Engineering, Search Engineering, Customer Experience, and senior technology leaders to deliver strategic initiatives ...

Cotality

Senior Machine Learning Scientist

Dallas, TX · On-site

The Senior Machine Learning Scientist develops advanced algorithms and models to extract valuable ... Data Analysis: Analyze large, diverse data sets to identify patterns, trends, and insights that ...

Quarterhill

Machine Learning Engineer

Frisco, TX · On-site

You'll work with large-scale image and video data, building and optimizing production-grade vision ... Contribute to our machine learning repositories and optimize models for performance, scalability ...

Photon

Machine Learning Engineer - NJ

Addison, TX · On-site

$54 - $71.50/hr

Apply expertise in data mining and machine learning techniques, including forecasting, prediction, segmentation, recommendation, and fraud detection. Data Engineering and Preparation: * Extend and ...

Vizient, Inc

AVP, Machine Learning & Modeling

Irving, TX · On-site

$157 - $290/hr

Oversee teams of data scientists, modelers, and ML engineers to deliver innovative and scalable ... Strategic Leadership and Vision Provide strategic direction for the organization's machine learning ...

Yum! Brands

Machine Learning Engineer II

Plano, TX · On-site

As a Machine Learning Engineer, you will play a crucial role in developing and deploying cutting ... data streaming. * Experience working with containerization technologies such as Docker and ...

Varsity Tutors

Machine Learning Tutor

Carrollton, TX · Remote

$18 - $40/hr

Guides students through data preprocessing, feature selection, building and comparing ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Dallas, TX · Remote

$18 - $40/hr

Guides students through data preprocessing, feature selection, building and comparing ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Plano, TX · Remote

$18 - $40/hr

Guides students through data preprocessing, feature selection, building and comparing ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Grand Prairie, TX · Remote

$18 - $40/hr

Guides students through data preprocessing, feature selection, building and comparing ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Arlington, TX · Remote

$18 - $40/hr

Guides students through data preprocessing, feature selection, building and comparing ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Irving, TX · Remote

$18 - $40/hr

Guides students through data preprocessing, feature selection, building and comparing ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

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All JobsMachine Learning Data Annotation JobsFreelance Machine Learning Data Annotation JobsFreelance Machine Learning Data Annotation Jobs in Dallas, TX

Freelance Machine Learning Data Annotation information

See Dallas, TX salary details

$12

$21

$34

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

As of Sep 4, 2026, the average hourly pay for freelance machine learning data annotation in Dallas, TX is $21.63, according to ZipRecruiter salary data. Most workers in this role earn between $17.12 and $24.71 per hour, depending on experience, location, and employer.

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 the key skills and qualifications needed to thrive as a freelance machine learning data annotation specialist?

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 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.

Can I work for freelance machine learning data annotation with no experience?

Freelance machine learning data annotation jobs often do not require prior experience, as many tasks involve simple labeling or categorization that can be learned quickly. Basic computer skills, attention to detail, and familiarity with annotation tools are helpful, and training is usually provided. However, building a portfolio or gaining some familiarity with data annotation platforms can improve job prospects.

What are the most commonly searched types of Machine Learning Data Annotation jobs in Dallas, TX?

The most popular types of Machine Learning Data Annotation jobs in Dallas, TX are:

What are popular job titles related to Freelance Machine Learning Data Annotation jobs in Dallas, TX?

For Freelance Machine Learning Data Annotation jobs in Dallas, TX, the most frequently searched job titles are:

What job categories do people searching Freelance Machine Learning Data Annotation jobs in Dallas, TX look for?

The top searched job categories for Freelance Machine Learning Data Annotation jobs in Dallas, TX are:

What cities near Dallas, TX are hiring for Freelance Machine Learning Data Annotation jobs?

Cities near Dallas, TX 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 Dallas, TX as of June 2026, with employment types broken down into 5% As Needed, 75% Full Time, 10% Part Time, and 10% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $44,993 per year, or $21.6 per hour.

Data Engineering Manager

HEB

Dallas, TX • On-site

Full-time

Re-posted 7 days ago

Job description

Responsibilities

We are seeking an experienced Data Engineering Manager to lead the design, development, and delivery of scalable data platforms and data products that power personalized customer experiences across digital retail channels. This leader will manage and develop a team of Data Engineers responsible for building reliable, secure, and high-performance data pipelines, machine learning data infrastructure, and customer data solutions that enable personalized product search, search ranking, recommendations, customer segmentation, behavioral analytics, and omnichannel personalization.

As a people leader, you will be responsible for hiring, onboarding, coaching, performance management, succession planning, and career development while fostering a culture of innovation, operational excellence, and continuous improvement. You will partner closely with Product Management, Data Science, Machine Learning Engineering, Search Engineering, Customer Experience, and senior technology leaders to deliver strategic initiatives that drive measurable business outcomes.

The ideal candidate combines deep expertise in modern data engineering and large-scale data platforms with proven leadership experience and a strong understanding of customer behavior data, personalization systems, recommendation engines, and cloud-based technologies.

Key Responsibilities & Essential FunctionsLeadership & Team Management
  • Lead, mentor, and develop a high-performing team of Data Engineers across one or more engineering squads.
  • Foster an environment of accountability, collaboration, innovation, and customer-centric thinking.
  • Manage all people leadership responsibilities, including hiring, onboarding, performance reviews, career development, promotions, succession planning, compensation planning, and employee engagement.
  • Coach and mentor engineers in engineering best practices, technologies, processes, and career growth.
  • Empower team members to be autonomous, highly effective, and capable of delivering scalable solutions.
  • Establish engineering standards, coding practices, operational excellence frameworks, and delivery processes.
  • Drive Agile planning, sprint execution, prioritization, and delivery of strategic initiatives.
Data Platform & Engineering
  • Lead the design, development, and operation of scalable batch, streaming, and real-time data platforms.
  • Develop and maintain data products supporting:
    • Personalized product search
    • Search relevance and ranking optimization
    • Product recommendations
    • Nice to have:
    • Customer segmentation
    • Customer identity and householding
    • Behavioral analytics
    • Omnichannel personalization
  • Design scalable data architectures utilizing modern lakehouse, data lake, and cloud-native patterns.
  • Build and support feature stores, APIs, and data services used by machine learning and personalization systems.
  • Ensure high levels of data quality, reliability, observability, governance, security, and compliance.
  • Optimize platform performance, scalability, availability, and cost efficiency.
  • Implement monitoring, alerting, SLA management, and incident response procedures for production data platforms.
Technical Strategy & Architecture
  • Develop technical roadmaps aligned with business priorities and long-term organizational objectives.
  • Lead the technical design and delivery of complex initiatives across multiple systems and platforms.
  • Recommend improvements to architecture, scalability, reliability, security, performance, and operational processes.
  • Evaluate emerging technologies and industry best practices to enhance platform capabilities.
  • Guide engineering teams on architectural decisions, code quality, design reviews, and technical standards.
  • Assist in diagnosing and resolving highly complex technical and operational issues.
Customer Personalization & Machine Learning Enablement
  • Build foundational data capabilities that support:
    • Product recommendation engines
    • Purchase behavior analysis
    • Real-time personalization
    • Search relevance optimization
    • Behavioral event processing
      Nice to Have:
    • Customer 360 platforms
    • Customer identity resolution
    • Clickstream analytics
  • Partner with Data Scientists and Machine Learning Engineers to operationalize and scale personalization models.
  • Enable experimentation, A/B testing, feature engineering, and measurement frameworks that improve customer experiences.
Cross-Functional Collaboration
  • Collaborate closely with Product Management, Data Science, Machine Learning Engineering, Search Engineering, Customer Experience teams, and business stakeholders.
  • Translate business objectives into scalable technical solutions and execution plans.
  • Communicate technical strategy, progress, risks, recommendations, and outcomes to leaders and stakeholders.
  • Lead cross-functional initiatives with significant business impact and organizational visibility.
Operational Excellence
  • Establish operational objectives, work plans, staffing strategies, and resource allocations.
  • Ensure adherence to budgets, timelines, and performance requirements.
  • Implement strategic policies, processes, and standards that support departmental and organizational objectives.
  • Drive continuous improvement through modern engineering practices, automation, observability, and operational excellence.
Qualifications & Key RequirementsWork Experience
  • 8+ years of experience in software engineering, data engineering, or related technical disciplines.
  • 3+ years of experience leading and developing engineering teams.
  • Proven experience delivering large-scale data platform, analytics, or machine learning infrastructure initiatives.
  • Experience managing technical roadmaps, cross-functional projects, and engineering delivery.
Knowledge, Skills & Abilities
  • Strong leadership skills with demonstrated success building and managing high-performing engineering teams.
  • Expert knowledge of data architecture, distributed systems, software design patterns, and engineering best practices.
  • Deep understanding of data modeling, ETL/ELT, streaming architectures, and event-driven systems.
  • Strong expertise with:
    • Python
    • SQL
    • Apache Spark
    • Kafka
    • Data orchestration frameworks
  • Experience with cloud platforms such as AWS and/or Google Cloud Platform.
  • Experience with modern data lake and lakehouse architectures.
  • Experience building APIs, data products, and services supporting machine learning applications.
  • Strong understanding of scalability, reliability, security, observability, and performance engineering.
  • Ability to lead technical strategy while balancing business priorities and organizational goals.
  • Strong communication and stakeholder management skills.
Preferred Qualifications
  • Experience in retail, e-commerce, digital commerce, or customer-facing digital products.
  • Experience supporting:
    • Personalized product search
    • Search ranking and relevance systems
    • Recommendation engines
    • Customer personalization platforms
    • Customer 360 initiatives
  • Experience working with clickstream, behavioral, transactional, and customer identity data.
  • Familiarity with:
    • Recommendation systems
    • Collaborative filtering
    • Embeddings and feature engineering
    • Vector search and semantic search technologies
    • MLOps platforms
    • Feature stores
    • Experimentation frameworks and A/B testing
  • Experience supporting machine learning platforms and production AI/ML workloads.
Education
  • Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related field, or equivalent combination of education and professional experience.
Physical Demands & Working Conditions
  • Ability to function in a fast-paced, multi-priority environment.
  • Ability to travel as needed.
  • May require occasional extended hours to support critical business initiatives and production events.

The responsibilities and qualifications outlined above describe the general nature and level of work assigned to this position and are not intended to be an exhaustive list of all duties, responsibilities, or skills required. Duties may be modified at any time based on business needs.


Last revised: 11/01/2024

Qualifications:UNAVAILABLEEducation:UNAVAILABLEEmployment Type: FULL_TIME

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