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Full Time Machine Learning Data Annotation Jobs in Arlington, TX

Crowe LLP

Machine Learning Intern

Dallas, TX · On-site

$27 - $42/hr

Important: the data science internship does not necessarily transition to a full-time role at the end of the period. Depending on team need, firm budget, and fit, interns should not expect their time ...

Crowe LLP

Machine Learning Intern

Plano, TX · On-site

$27 - $42/hr

Important: the data science internship does not necessarily transition to a full-time role at the end of the period. Depending on team need, firm budget, and fit, interns should not expect their time ...

Resolve Tech Solutions

Senior ML Engineer

Addison, TX

$101K - $138K/yr

Develop machine learning models and algorithms to address business needs. Collaborate with data scientists and software engineers to design and implement scalable and efficient solutions. Clean ...

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All JobsMachine Learning Data Annotation JobsFull Time Machine Learning Data Annotation JobsFull Time Machine Learning Data Annotation Jobs in Arlington, TX

Full Time Machine Learning Data Annotation information

See Arlington, TX salary details

$33.7K

$110.5K

$176.8K

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

As of Sep 4, 2026, the average yearly pay for full time machine learning data annotation in Arlington, TX is $110,458.00, according to ZipRecruiter salary data. Most workers in this role earn between $88,600.00 and $122,400.00 per year, depending on experience, location, and employer.

What is a full time machine learning data annotation job?

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 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 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 popular job titles related to Full Time Machine Learning Data Annotation jobs in Arlington, TX?

For Full Time Machine Learning Data Annotation jobs in Arlington, TX, the most frequently searched job titles are:

What job categories do people searching Full Time Machine Learning Data Annotation jobs in Arlington, TX look for?

The top searched job categories for Full Time Machine Learning Data Annotation jobs in Arlington, TX are:

What cities near Arlington, TX are hiring for Full Time Machine Learning Data Annotation jobs?

Cities near Arlington, 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 Arlington, TX as of July 2026, with employment types broken down into 23% Full Time, 4% Part Time, 67% Contract, and 6% Nights. Highlights an 4% Physical, and 96% Remote job distribution, with an average salary of $110,458 per year, or $53.1 per hour.

Senior Data Scientist

Fidelity Investments

Westlake, TX • On-site

Full-time

Re-posted 17 days ago

Fidelity Investments rating

8.7

Company rating: 8.7 out of 10

Based on 273 frontline employees who took The Breakroom Quiz

16th of 154 rated financial services

Job description


Senior Data Scientist - Applied AI, NLP, and LLM Solutions
Note: Fidelity will not provide immigration sponsorship for this position
Fidelity Workplace Investing is seeking hands-on, builder-oriented Senior Data Scientists with experience in applied AI, natural language processing, large language models, machine learning, and knowledge graph technologies. This position will be based full time in either Westlake, TX or Merrimack, NH.
The Purpose of Your Role
This individual will lead high-profile applied data science and artificial intelligence initiatives across Workplace Investing, working closely with Technology, Product Management, AI/ML Engineering, and others. The role will focus on developing and evaluating AI-based solutions using natural language processing (NLP), large language models (LLM), machine learning (ML), knowledge graphs, agentic AI patterns, and other advanced or emerging techniques. Key assignments may include document processing and information extraction, schema mapping, enterprise assistants, recommender systems, and anomaly detection.
The successful candidate must be comfortable operating in a fast-paced and sometimes ambiguous environment working with current and emerging AI technologies. They will be expected to gather and analyze data from multiple structured and unstructured data sources, develop reliable models and evaluation frameworks, interpret and clearly communicate findings to technical and business audiences. They will support a broad range of applied AI initiatives with the highest degree of quality, partner effectively with engineering teams to move solutions into production, and thrive in a high-performing, collaborative work environment. The ideal candidate combines strong data science fundamentals with product instincts, technical curiosity, and a track record of delivering measurable business impact.
The Skills You Bring
  • PhD in Computer Science, Information Science, Statistics, or a related STEM discipline with focus on AI, machine learning, natural language processing, deep learning, knowledge graphs, or related methods; OR a Master's Degree in a related field with 3 or more years relevant professional experience
  • Strong technical foundation in machine learning and statistical modeling, with deeper experience in one or more applied AI areas such as natural language processing, large language models, deep learning, knowledge graphs, or related methods.
  • Strong Python and SQL programming skills with demonstrated proficiency in data extraction, data engineering, exploratory analysis, feature engineering, data modeling, pipeline automation, and model evaluation.

  • Solid verbal communication, presentation, and technical writing skills with an ability to explain complex data science, statistics, and computer science concepts clearly to nontechnical audiences.
  • Experience or working knowledge in one or more applied AI areas such as information retrieval, question answering, chatbot evaluation, retrieval-augmented generation, or agentic AI frameworks.
  • Exposure to intelligent document processing use cases, which may include document classification, OCR, key-value extraction, signature or seal detection, annotation strategy and dataset creation, and evaluation of extraction quality.
  • Working knowledge of embedding models, vector representations, semantic similarity clustering, or dimensionality reduction techniques such as t-SNE or UMAP.
  • Experience in one or more predictive modeling areas such as recommendation systems, ranking models, ensemble methods, anomaly detection, statistical process control, time-series monitoring, threshold strategies, or alert-quality evaluation.
  • Experience designing or contributing to AI/ML evaluation and monitoring frameworks, including benchmark datasets, labeled and synthetic test data, model and prompt comparison, precision/recall analysis, error analysis, latency assessment, cost-quality tradeoff analysis, and production monitoring with tools such as Fiddler.

The Value You Deliver
  • Lead the data science and model development components of projects involving large language models, natural language processing, knowledge graphs, and related applied techniques.
  • Design, build, and deploy applied AI solutions across NLP, LLMs, document processing, schema mapping, recommendation, and anomaly detection use cases.
  • Lead data analysis with diverse scope and complex business and technical challenges
  • Develop best practices for data science, considering the full analytical lifecycle
  • Ensure the delivery of high-quality, trustworthy data science by developing guidelines and rigorous evaluation frameworks for AI/ML solutions.
  • Implement new technologies in a production environment with product, IT, and data engineering teams
  • Present reports and findings to senior-level technical and nontechnical audiences

How Your Work Impacts the Organization
As a data scientist in Fidelity Workplace Investing, you will contribute to advancing the analytics and data science capability for a variety of employee benefit products and will take the organization to the next level.
Fidelity's Onsite Working Model
Fidelity is transitioning to a full-time onsite working model through a phased rollout across regions and roles. Currently, some roles and locations require 100% onsite presence, while others require less. Onsite expectations are likely to evolve as the rollout continues. This transition does not apply to fully remote roles.
Certifications:
Category:
Data Analytics and Insights
Please be advised that Fidelity's business is governed by the provisions of the Securities Exchange Act of 1934, the Investment Advisers Act of 1940, the Investment Company Act of 1940, ERISA, numerous state laws governing securities, investment and retirement-related financial activities and the rules and regulations of numerous self-regulatory organizations, including FINRA, among others. Those laws and regulations may restrict Fidelity from hiring and/or associating with individuals with certain Criminal Histories.

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