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Product Development Intern - AI & ML Engineering

Modernizing MedicineBoca Raton, FL

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Job Description

ModMed is seeking a highly motivated and research-driven AI/ML Intern to join our AI & Machine Learning team for Summer 2026. This internship will focus on advancing intelligent automation across the clinical and healthcare claims data lifecycle. The intern will work with cutting-edge generative AI and large language models (LLMs) to derive insights from claim data, evaluate claim states, and support the development of AI-driven decision support systems that recommend next-best actions throughout the claim workflow.

Your Role:

  • Develop and evaluate machine learning and generative AI models for clinical and healthcare claims data.
  • Apply LLM and NLP techniques to extract insights, classify claim states, and recommend operational actions.
  • Support model serving, deployment, and end-to-end ML workflow integration with engineering teams.
  • Collaborate with data scientists, ML engineers, and product teams to guide research direction and deliver prototypes.

Skills & Requirements:

  • Currently pursuing a PhD or Master's degree in Computer Science, Electrical/Computer Engineering, Statistics, Data Science, Computational Biology, or a related field (Required)
  • Strong foundation in Machine Learning (Required)
  • Strong foundation in Generative AI / Large Language Models (Required)
  • Strong foundation in Statistics (Required)
  • Strong foundation in Natural Language Processing (Required)
  • Strong foundation in Deep Learning (Required)
  • Experience with model serving, deployment, or end-to-end ML workflows (preferred)
  • Demonstrated research experience (e.g., peer-reviewed publications, preprints, research projects) (Required)
  • Experience working with healthcare claims or electronic health records (Preferred)
  • Familiarity with AWS, Databricks, and distributed data environments (Preferred)
  • Experience with LLM evaluation, safety, and fairness (Preferred)
  • Exposure to Agentic AI or autonomous decision-making frameworks (Preferred)

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