Our Company's Computational Toxicology group integrates toxicological expertise, data science, and AI/ML, including emerging Large Language Model (LLM) applications, to improve the safety assessment of drug candidates, accelerate decision-making, and reduce reliance on animal testing.
Requirements
- Must be proficient in Python; solid with statistics/ML and data wrangling (pandas/SQL).
- Must have hands-on with scikit-learn and at least one deep learning framework (PyTorch or TensorFlow).
- Must be comfortable with Git/GitHub and reproducible workflows.
- Must have 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).
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.
Other
- Pursuing BS/MS/PhD in Computational Science, Data Science, Bioinformatics, Computational Biology/Chemistry, Computational Linguistics, or related field.
- Must be available for a period of 12 weeks, beginning June 2026.
- No relocation
- No Travel Required
- No VISA Sponsorship
