Intern, Data Science, Machine Learning & AI-Remote

Role Overview

Internship Schedule

• The internship requires 20 to 25 hours per week.
• The internship duration is January 25, 2027 through May 7, 2027.

Workplace

• The internship is remote and remote work must be performed inside the United States.
• A reliable WiFi connection is required.

Compensation

• The internship pays $23.00 per hour.

Responsibilities

• Support LLM-powered applications and agentic AI workflows for clinical, biomedical, and operational research.
• Apply LLMs to information extraction, classification, summarization, question answering, and workflow orchestration.
• Experiment with prompting, structured outputs, retrieval-augmented generation, embeddings, vector search, tool use, and agent workflows.
• Prepare, clean, organize, and document data for LLM and generative AI experiments.
• Develop reproducible Python prototypes and workflows using approved AI and machine learning tools.
• Evaluate, validate, and test LLM systems, including error analysis, robustness testing, and alternative comparisons.
• Assess hallucination, factual consistency, relevance, reliability, bias, privacy, and reproducibility.
• Document methods, prompts, evaluation results, limitations, and improvements.
• Contribute to technical documentation, presentations, abstracts, manuscripts, and project discussions.

Qualifications

• Currently pursuing an MS or PhD in a quantitative field such as computer science, artificial intelligence, data science, statistics, engineering, or public health.
• Coursework or project experience with LLMs, generative AI, natural language processing, retrieval-augmented generation, or AI agents.
• Experience with APIs or open-source frameworks for building and testing LLM applications.
• Python programming experience and familiarity with data analysis or machine learning libraries.
• Ability to apply core LLM concepts including tokens, context windows, embeddings, prompting, retrieval, and generation.
• Strong analytical, organizational, problem-solving, communication, and collaboration skills.
• Experience with cloud or high-performance computing environments is advantageous.
• Healthcare, clinical, or biomedical dataset experience is preferred.
• Familiarity with model evaluation, experimental design, error analysis, version control, or reproducible workflows is advantageous.
• Authorization to work in the United States without sponsorship is required.

Program Outcomes