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Artificial Intelligence Machine Learning Internship Jobs in Dallas, TX

builders

IT Internship - Irving, TX (Summer 2027)

Irving, TX

$14.50 - $19.25/hr

Artificial Intelligence / Machine Learning * Software Engineering * Master's degree candidates in ... Basic programming knowledge WHY THIS INTERNSHIP? As Builders FirstSource continues to expand the ...

Aivra Health LLC

AI/ML Engineer

Dallas, TX · On-site

... machine learning and artificial intelligence solutions. The ideal candidate should have strong ... internships, academic projects, research, GitHub repositories, or professional experience ...

builders

IT Internship - Remote (Summer 2027)

Irving, TX · On-site +1

This internship provides hands-on experience applying data-driven solutions to business challenges ... Interest in Artificial Intelligence, Machine Learning, Data Science, Master Data Management, and ...

Aivra Health LLC

AI/ML Engineer

Dallas, TX · On-site

Artificial Intelligence * Machine Learning * Data Science * Computer Engineering * Software ... AI/ML internships, research, or university projects * GitHub/portfolio demonstrating practical AI ...

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All JobsArtificial Intelligence Machine Learning JobsArtificial Intelligence Machine Learning Internship JobsArtificial Intelligence Machine Learning Internship Jobs in Dallas, TX

Artificial Intelligence Machine Learning Internship information

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$25.2K

$42.1K

$87.1K

How much do artificial intelligence machine learning internship jobs pay per year?

As of Sep 7, 2026, the average yearly pay for artificial intelligence machine learning internship in Dallas, TX is $42,125.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,200.00 and $45,500.00 per year, depending on experience, location, and employer.

What is an artificial intelligence machine learning internship?

An Artificial Intelligence (AI) Machine Learning (ML) Internship is a temporary position designed for students or recent graduates to gain hands-on experience in AI and ML fields. Interns work on real-world projects involving data analysis, model development, and the application of machine learning algorithms under the guidance of experienced professionals. These internships help individuals develop practical skills, build a professional network, and improve their understanding of current technologies and tools used in AI and ML. Interns may also participate in research, code reviews, and present their findings. This experience is valuable for pursuing a career in data science, AI engineering, or related fields.

What are the key skills and qualifications needed to thrive as an artificial intelligence machine learning intern?

To thrive as an Artificial Intelligence Machine Learning Intern, you need foundational knowledge in computer science, statistics, and mathematics, often supported by coursework or a degree in a related field. Familiarity with programming languages like Python, machine learning libraries such as TensorFlow or PyTorch, and experience using data analysis tools are typically required. Strong problem-solving abilities, curiosity, and effective communication skills help interns excel in collaborative, fast-paced environments. These skills and qualities are essential to contribute meaningfully to projects, learn quickly, and adapt to evolving technological challenges.

What types of projects can an artificial intelligence machine learning intern expect to work on during their internship?

As an AI/ML intern, you can expect to work on a variety of projects such as data preprocessing, model development, and performance evaluation. Interns often assist in building and testing machine learning models, analyzing large datasets, and contributing to the improvement of existing algorithms. You may also collaborate closely with data scientists, engineers, and product teams to solve real-world problems and help integrate AI solutions into business processes. These experiences provide valuable exposure to both research and practical applications in the field.

What is the difference between Artificial Intelligence Machine Learning Internship vs Data Science Internship?

AspectArtificial Intelligence Machine Learning InternshipData Science Internship
Required CredentialsBasic programming, math, and AI/ML knowledgeStatistics, programming, and data analysis skills
Work EnvironmentTech companies, research labs, startupsBusiness, finance, healthcare, tech firms
Employer & Industry UsageAI/ML-focused roles in tech and researchData analysis and insights across industries
Search & Comparison IntentUnderstanding AI/ML internship roles and skillsExploring data science internship opportunities

Artificial Intelligence Machine Learning internships focus on developing AI and ML models, requiring programming and math skills. Data Science internships emphasize analyzing data to generate insights, often involving statistics and data visualization. While both roles involve data and programming, AI/ML internships are more specialized in building intelligent systems, whereas Data Science internships focus on interpreting data for decision-making.

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

The most popular types of Artificial Intelligence Machine Learning jobs in Dallas, TX are:

What are popular job titles related to Artificial Intelligence Machine Learning Internship jobs in Dallas, TX?

For Artificial Intelligence Machine Learning Internship jobs in Dallas, TX, the most frequently searched job titles are:

What job categories do people searching Artificial Intelligence Machine Learning Internship jobs in Dallas, TX look for?

The top searched job categories for Artificial Intelligence Machine Learning Internship jobs in Dallas, TX are:

What cities near Dallas, TX are hiring for Artificial Intelligence Machine Learning Internship jobs?

Cities near Dallas, TX with the most Artificial Intelligence Machine Learning Internship job openings:

Artificial Intelligence Machine Learning Internship jobs near you
Infographic showing various Artificial Intelligence Machine Learning Internship job openings in Dallas, TX as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 22% Part Time, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $42,125 per year, or $20.3 per hour.

Executive Director - Applied Artificial Intelligence Machine Learning

JPMorgan Chase & Co.

Plano, TX • On-site

$250/hr

Other

Re-posted 29 days ago

JPMorgan Chase & Co. rating

7.9

Company rating: 7.9 out of 10

Based on 500 frontline employees who took The Breakroom Quiz

77th of 175 rated banks

Job description

As an Applied AI/ML Executive Director within our dynamic team, you will apply your quantitative, data science, and analytical skills to complex problems. As a Machine Learning Director, you will have the opportunity to apply sophisticated machine learning methods to complex tasks including natural language processing, speech analytics, time series, reinforcement learning and recommendation systems. You will collaborate with various teams and actively participate in our knowledge sharing community. We are looking for someone who excels in a highly collaborative environment, working together with our business, technologists and control partners to deploy solutions into production. If you have a strong passion for machine learning and enjoy investing time towards learning, researching and experimenting with new innovations in the field, this role is for you.

Job responsibilities
  • Develop advanced agentic AI solutions involving structured and unstructed data, casual analytics, machine learning, deep learning, reinforcement learning, and optimization.
  • Design robust agent architectures combining LLM reasoning with tools, structured data, and APIs spanning state, memory, and context management, plus loop engineering (plan/act/observe, verification, termination, and fallback/escalation).
  • Engineer reliable agent-driven workflows emphasizing correctness, traceability, and control-aware behavior (guardrails, approvals, auditable decision paths).
  • Build knowledge-centric reasoning layers, including knowledge graphs and hybrid retrieval (RAG + graph + structured sources) to improve grounding and accuracy.
  • Drive specification-driven development: author specs and contracts (schemas, validators, tool/skill interfaces) and build evaluation/regression harnesses.
  • Advance agent quality via recursive self-improvement through automated evaluation and critique loops, red-team feedback, skill/prompt instruction optimization, and outcome-driven dataset curation (human-in-the-loop as needed).
  • Coach and mentor AI/ML team members, setting a high bar for engineering rigor and research depth.
Required qualifications, capabilities, and skills
  • PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science Or with at least 5 years of industry experience or an MS with at least 7 years of industry or research experience in the field.
  • Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
  • Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
  • Experience with big data and scalable model training and solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences.
  • Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments

  • Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences. Curious, hardworking and detail-oriented, and motivated by complex analytical problems

Preferred qualifications, capabilities , and skills:
  • Strong background in Mathematics and Statistics and familiarity with the financial services industries and continuous integration models and unit test development
  • Knowledge in search/ranking, Reinforcement Learning or Meta Learning
  • Experience with A/B experimentation and data/metric-driven product development, cloud-native deployment in a large scale distributed environment and ability to develop and debug production-quality code
  • Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal
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