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Full Time Machine Learning Data Annotation Jobs in Dallas, TX

Ascentt

Senior Machine Learning Engineer

Plano, TX · On-site

$100K - $137K/yr

Data Science / Engineering Employment Type: Full-time About the Role: We are looking for an experienced Senior Machine Learning Engineer with deep expertise in statistical and machine learning ...

Ascentt

Senior Machine Learning Engineer

Plano, TX · On-site

$100K - $137K/yr

We turn enterprise data into real-time decisions using advanced machine learning and GenAI. Our team solves hard engineering problems at scale, with real-world industry impact. We're hiring ...

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 ...

Capital One

Lead Machine Learning Engineer

Plano, TX · On-site +1

$98K - $129K/yr

Construct optimized data pipelines to feed ML models. * Leverage continuous integration and ... The minimum and maximum full-time annual salaries for this role are listed below, by location.

RealPage, Inc.

Data Scientist

Richardson, TX · Remote

$116K - $198K/yr

Machine Learning & Data Science * Develop, evaluate, and deploy predictive and generative models for real production use cases * Perform feature engineering and data preparation for modeling ...

Vangard, Inc.

Machine Learning Engineer, Specialist

Dallas, TX · On-site

$113K - $136K/yr

Develops complex data pipelines and implements data engineering design principles for iterative ... Serves as a machine learning engineering subject matter expert on cross functional teams for large ...

Intuit

Staff Machine Learning Engineer

Plano, TX · On-site

Important skills include creating data pipelines, developing and deploying models, and machine learning operations. Responsibilities * Work with AI scientists to create and refine features from the ...

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Full Time Machine Learning Data Annotation information

See Dallas, TX salary details

$37.1K

$121.4K

$194.4K

How much do full time machine learning data annotation jobs pay per year?

As of Jun 5, 2026, the average yearly pay for full time machine learning data annotation in Dallas, TX is $121,417.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,400.00 and $134,500.00 per year, depending on experience, location, and employer.

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

To thrive as a Full Time Machine Learning Data Annotation Specialist, you need strong attention to detail, basic data literacy, and familiarity with data labeling concepts, often supported by a high school diploma or equivalent. Proficiency in specialized annotation platforms, spreadsheet tools, and sometimes knowledge of Python or labeling frameworks is typically required. Reliability, patience, and effective communication are valuable soft skills for ensuring accuracy and collaborating with team members. These skills and qualities are crucial because they directly impact the quality of training data, which is essential for developing effective machine learning models.

What are Full Time Machine Learning Data Annotation jobs?

Full time machine learning data annotation jobs involve labeling, tagging, or categorizing data such as images, text, audio, or video to help train machine learning models. Data annotators play a crucial role in ensuring that AI systems learn from high-quality, accurately labeled datasets. These positions often require attention to detail, consistency, and sometimes familiarity with the subject matter or specialized tools. Full-time roles may be remote or onsite and can span industries like autonomous vehicles, healthcare, retail, and more.

What are some common challenges faced by machine learning data annotators, and how are these typically addressed within a team?

Machine learning data annotators often encounter challenges such as maintaining consistency in labeling, handling ambiguous data, and meeting tight deadlines for large datasets. Teams usually address these by establishing clear annotation guidelines, conducting regular training sessions, and implementing quality assurance processes like peer reviews and spot checks. Collaboration with data scientists and project managers is also common, ensuring that annotators can ask questions and clarify uncertainties, leading to higher-quality labeled data and a supportive work environment.

What is the difference between Full Time Machine Learning Data Annotation vs Data Labeling Specialist?

AspectFull Time Machine Learning Data AnnotationData Labeling Specialist
CredentialsHigh school diploma or equivalent; some roles prefer technical certificationsHigh school diploma or equivalent; training often provided on the job
Work EnvironmentOffice or remote; collaborative with data science teamsRemote or office; focused on labeling tasks
Industry UsageUsed across AI/ML companies, tech firms, and startupsCommon in AI/ML, data services, and outsourcing companies
Job FocusCreating labeled datasets for machine learning modelsAnnotating data such as images, videos, or text for AI training

Full Time Machine Learning Data Annotation involves creating high-quality labeled datasets for AI models, often requiring technical understanding. Data Labeling Specialists focus on annotating data accurately, typically with less emphasis on technical skills. Both roles are essential in AI development but differ mainly in scope and technical complexity.

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 Full Time Machine Learning Data Annotation jobs in Dallas, TX? For Full Time Machine Learning Data Annotation jobs in Dallas, TX, the most frequently searched job titles are:
What job categories do people searching Full Time Machine Learning Data Annotation jobs in Dallas, TX look for? The top searched job categories for Full Time Machine Learning Data Annotation jobs in Dallas, TX are:
What cities near Dallas, TX are hiring for Full Time Machine Learning Data Annotation jobs? Cities near Dallas, TX with the most Full Time Machine Learning Data Annotation job openings:
Full Time Machine Learning Data Annotation jobs near you
Infographic showing various Full Time Machine Learning Data Annotation job openings in Dallas, TX as of May 2026, with employment types broken down into 1% As Needed, 73% Full Time, 20% Part Time, and 6% Contract. Highlights an 46% Physical, 1% Hybrid, and 53% Remote job distribution, with an average salary of $121,417 per year, or $58.4 per hour.
Senior Machine Learning Engineer

Senior Machine Learning Engineer

Ascentt

Plano, TX • On-site

$100K - $137K/yr

Full-time

Posted 29 days ago

Job description

Ascentt is building cutting-edge data analytics & AI/ML solutions for global automotive and manufacturing leaders. We turn enterprise data into real-time decisions using advanced machine learning and GenAI. Our team solves hard engineering problems at scale, with real-world industry impact. We're hiring passionate builders to shape the future of industrial intelligence.
Job Title: Senior Machine Learning Engineer
Location: Ann Arbor, Michigan
Experience Level: 7+ Years
Department: Data Science / Engineering
Employment Type: Full-time
About the Role:
We are looking for an experienced Senior Machine Learning Engineer with deep expertise in statistical and machine learning techniques, large-scale data processing, and model deployment in cloud environments. The ideal candidate will be a self-starter with strong problem-solving skills and hands-on experience in building and deploying ML models using big data technologies like PySpark and cloud platforms like Amazon SageMaker.
Key Responsibilities:
  • Design, develop, and deploy scalable machine learning models for real-world business problems using structured and unstructured data.
  • Analyze large datasets using PySpark and other distributed computing frameworks to extract insights and prepare features for ML pipelines.
  • Apply a wide range of statistical, machine learning, and deep learning techniques, including but not limited to regression, classification, clustering, time-series forecasting, and NLP.
  • Own end-to-end ML pipelines from data ingestion, preprocessing, training, validation, tuning, and deployment.
  • Utilize Amazon SageMaker or similar platforms for building, training, and deploying models in a production-grade environment.
  • Collaborate closely with data engineers, data scientists, and product teams to integrate models with business workflows.
  • Monitor and improve model performance, scalability, and reliability in production.
  • Contribute to setting up and maintaining the ML environment and tooling (including environment configuration, CI/CD pipelines for ML, model versioning, etc.).

Required Qualifications:
  • 7+ years of experience in machine learning, data science, or related fields.
  • Strong programming skills in Python with experience in ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
  • Hands-on experience with PySpark for big data processing and model development.
  • Proficient in building models on large-scale datasets (terabytes to petabytes).
  • Solid understanding of statistical analysis, probability, hypothesis testing, and experimental design.
  • Experience with Amazon SageMaker (or similar cloud-based ML platforms).
  • Strong knowledge of ML Ops practices including version control, model monitoring, and retraining strategies.
  • Familiarity with containerization (Docker) and CI/CD practices for ML projects is a plus.
  • Excellent communication skills and the ability to clearly explain complex concepts to non-technical stakeholders.

Preferred Qualifications:
  • Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative discipline.
  • Experience with workflow orchestration tools (e.g., Airflow, Kubeflow).
  • Prior experience in domains like Manufacturing, finance, healthcare, or e-commerce is a plus.

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