Intern, Data Science, Machine Learning & AI-Remote

American Heart Association • Dallas, TX

2d • Remote

About The Position

The American Heart Association’s Internship Program provides college students an opportunity for hands-on experience in various facets for individuals interested in gaining work experience with a non-profit, voluntary health organization. This internship supports the Data Science team and provides hands-on experience in machine learning and artificial intelligence, with a primary focus on large language models (LLMs), generative AI, and agentic AI. The intern will build foundational knowledge of how LLMs work and apply LLMs to modeling, analytical workflows, and research use cases. Working with data scientists and cross-functional partners, the intern will help develop reproducible AI-enabled workflows and learn how to evaluate, validate, and document LLM-based solutions for clinical and biomedical research settings. The Association offers many resources to help you maintain work-life harmonization through your changing needs and life situations. To help you be successful, you will have access to Heart U, our award-winning corporate university, as well as training and support locally and through our National Center. #TheAHALife is more than a company culture; it is our way of life. It embodies our commitment to work-life harmonization and is guided by our core values where our employees can thrive both personally and professionally. Discover why you will Be Seen. Be Heard. Be Valued at the American Heart Association by following us on LinkedIn , Instagram , Facebook , X , and at heart.jobs .

Requirements

• Currently pursuing an MS or PhD degree in Computer Science, Artificial Intelligence, Biomedical Informatics, Data Science, Statistics, Engineering, Public Health, or a related quantitative field
• Coursework or project experience with LLMs, generative AI, natural language processing, retrieval-augmented generation, or AI agents.
• Experience using APIs or open-source frameworks to build and test LLM applications.
• Programming experience in Python and familiarity with common data analysis or machine learning libraries.
• Ability to understand and apply core LLM concepts such as tokens, context windows, embeddings, prompting, retrieval, and generation.
• Strong analytical, problem-solving, organizational, and attention-to-detail skills.
• Commitment to reproducible research, responsible AI practices, data quality, and clear documentation.
• Ability to communicate effectively and collaborate with both technical and non-technical colleagues.
• Intermediate to excellent proficiency in Microsoft Word, Excel, Outlook, PowerPoint.
• Must be legally authorized to work in the United States for any employer without sponsorship, now or in the future.
• For any roles working remotely, the work must also be performed inside the United States, not in a foreign country.

Nice To Haves

• Experience working with cloud computing and/or high-performance computing environments (e.g., AWS, Snowflake, Azure, GCP).
• Familiarity with model evaluation, experimental design, error analysis, version control, or reproducible workflow practices.
• Experience with healthcare, clinical, or biomedical datasets is preferred.
• Ability to work in a fast-paced, dynamic environment managing multiple priorities involving multiple entities.

Responsibilities

• Support the design and development of LLM-powered applications and agentic AI workflows for clinical, biomedical, and operational research use cases.
• Apply LLMs to modeling and analytical tasks, including information extraction, classification, summarization, question answering, and workflow orchestration.
• Experiment with prompt design, structured outputs, retrieval-augmented generation, embeddings, vector search, tool use, and multi-step agent workflows.
• Prepare, clean, organize, and document data used in LLM and generative AI experiments.
• Develop reproducible prototypes and workflows using Python and approved AI/ML tools and platforms.
• Design and perform evaluations of LLM systems using clearly defined criteria and appropriate quantitative and qualitative measures.
• Conduct validation, error analysis, robustness testing, and comparison of model or workflow alternatives.
• Assess issues such as hallucination, factual consistency, relevance, reliability, bias, privacy, and reproducibility.
• Document methods, assumptions, prompts, evaluation results, limitations, and recommended improvements.
• Contribute to technical documentation, presentations, abstracts, manuscripts, and cross-functional project discussions.

Benefits

• Access to Heart U, our award-winning corporate university
• Training and support locally and through our National Center
• Opportunity to participate in our Teladoc General Medical and Behavioral Health programs
• Access to our Employee Assistance Program (EAP) at no cost

What This Job Offers

• Job Type: Part-time
• Career Level: Intern