AI/ML Toxicology Intern: Multimodal Data & NLP

Merck; West Point (PA); On-site; USD 39,000 - 111,000; Full time

Job summary

Merck & Co. invites motivated summer interns to join the AI/ML Computational Toxicology team in the Research and Development Division. The program offers up to 12 weeks of hands-on projects applying machine learning, NLP, and LLMs to multimodal toxicology data across genomics, chemistry, and literature.

You will develop predictive models, mine unstructured reports, and present dashboards, gaining exposure to cutting-edge toxicology research and data science workflows.

Qualifications

• Pursuing BS/MS/PhD in Computational Science, Data Science, Bioinformatics, Computational Biology/Chemistry, Computational Linguistics, or related field.
• Proficient in Python; solid with statistics/ML and data wrangling (pandas/SQL)
• Hands-on with scikit-learn and at least one deep learning framework (PyTorch or TensorFlow)
• Comfortable with Git/GitHub and reproducible workflows
• Experience with NLP and LLM tools (e.g., Hugging Face, spaCy, NLTK) and techniques such as prompt design, fine-tuning, and retrieval-augmented generation (RAG)
• Must be available for a period of 10-12 weeks, beginning June 2027

Responsibilities

• Build and evaluate predictive models for toxicity risk and mechanistic hypotheses generation using multimodal pharmaceutical data
• Develop NLP/LLM pipelines (prompting, fine‑tuning, RAG) to mine unstructured reports and literature
• Prepare dashboards (e.g., Streamlit) and present results to scientists and leadership

Skills

Python

ML

Pandas/SQL

Git/GitHub

NLP

LLMs

PyTorch

TensorFlow

Education

BS/MS/PhD in Computation/Data Science/Related Field

Tools

Hugging Face

spaCy

NLTK

scikit-learn

PyTorch

TensorFlow

Job description

Merck & Co. invites motivated summer interns to join the AI/ML Computational Toxicology team in the Research and Development Division. The program offers up to 12 weeks of hands-on projects applying machine learning, NLP, and LLMs to multimodal toxicology data across genomics, chemistry, and literature.

You will develop predictive models, mine unstructured reports, and present dashboards, gaining exposure to cutting-edge toxicology research and data science workflows.