Intern, Data Science, Machine Learning & AI-Remote
American Heart Association alt · Dallas, TX, US
About this role
**Intern, Data Science, Machine Learning & AI (Remote)** **Overview** Since 1924, the American Heart Association has helped cut cardiovascular disease deaths in half—and there’s still more to do. Join the AHA Internship Program to gain hands-on experience with the Data Science team, focusing on **large language models (LLMs)**, **generative AI**, and **agentic AI**. **Internship Details** - **Time Commitment:** 20–25 hours/week - **Duration:** 1/25/27 – 5/7/27 - **Location:** Remote (work must be performed inside the United States) - **Pay:** $23.00/hour **What You’ll Learn / Outcomes** - Foundational LLM & generative AI concepts (prompting, embeddings, RAG, tool use, agentic workflows) - Hands-on application of LLMs for modeling, workflow automation, information extraction, summarization, and research use cases - Structured evaluation/validation of LLM outputs, workflows, and applications - Reproducible AI development, documentation, testing, and responsible AI practices - Collaboration with multidisciplinary teams and communication to technical + non-technical audiences **Key Responsibilities** *(may include)* - Support design and development of **LLM-powered applications** and **agentic AI workflows** for clinical, biomedical, and operational research - Apply LLMs to tasks such as **information extraction, classification, summarization, question answering, and workflow orchestration** - Experiment with prompt design, structured outputs, RAG, embeddings/vector search, tool use, and multi-step agent workflows - Prepare, clean, organize, and document data for LLM/generative AI experiments - Build reproducible prototypes/workflows using **Python** and approved AI/ML tools/platforms - Perform evaluations (quantitative + qualitative), validation, error analysis, robustness testing, and comparisons - Assess hallucinations, 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 discussions **Qualifications** - Currently pursuing an **MS or PhD** in CS, AI, Biomedical Informatics, Data Science, Statistics, Engineering, Public Health, or a related quantitative field - Coursework or project experience with LLMs, generative AI, NLP, RAG, or AI agents - Experience using **APIs** or open-source frameworks to build/test LLM applications - Programming experience in **Python** and familiarity with common data analysis/ML libraries - Ability to understand/apply core LLM concepts (tokens, context windows, embeddings, prompting, retrieval, generation) - Strong analytical, problem-solving, organizational, and detail-oriented skills - Commitment to reproducible research, responsible AI, data quality, and clear documentation - Effective communication and collaboration with technical and non-technical colleagues - Experience with cloud/HPC environments (e.g., **AWS, Snowflake, Azure, GCP**) preferred - Familiarity with evaluation, experimental design, error analysis, version control, and reproducible workflow practices - Healthcare/clinical/biomedical dataset experience preferred **Required Equipment / Work Authorization** - Reliable WiFi connection - Minimum availability: **20 hrs/week**, M–F between **8:30am–5pm** - Must be legally authorized to work in the **United States** **without sponsorship** (now or in the future) -
Listing freshness
CronJobs last confirmed this listing 49m ago. If its source stops confirming the opening for seven days, this page is removed from active inventory.