2027 Future Talent Program - Computer Scientist - Agentic & Augmented Authoring Systems - Co-op

Job Description The Future Talent Program features Co-operative (Co-op) education that lasts up to 6 months and will include one or more projects. These opportunities in our Research and Development Division can provide you with great development and a chance to see if we are the right company for your long-term goals. Nonclinical Drug Safety (NDS) helps advance high-quality drug candidates into development by defining the nonclinical safety and selectivity of lead compounds.

NDS employees evaluate Lead Op candidates and preclinical toxicity of drug development candidates, providing a mechanistic understanding of drug-induced toxicity and assessing implications for human safety. NDS provides collaborative research in animal model development, veterinary medical and animal care, and research facility management. NDS also responds to regulatory questions in support of drug registration.

The Digital Operations and Innovation (DOI) team within NDS Operations comprises a talented group of data scientists dedicated to leveraging scientific and operational data to reduce administrative burden, improve the findability and accessibility of data, and discover and implement innovative applications of AI to modernize and advance our business. Position Overview:   We seek a talented and motivated Computer Science Co-op to join our team for a six-month role.

This position will contribute to our efforts to develop an agentic, augmented authoring system supporting Nonclinical Drug Safety (NDS). The goal of this effort is to drive towards a "zero-draft" process that retrieves, combines, and formats key data from structured, semi-structured, and unstructured sources into a consistent template for our scientists. The ideal candidate will actively participate in regular sprints to design and refine agents, propose creative solutions to agent-based workflows, and work closely with scientific and operational SMEs.

In this role, you will be a valued contributor to a visible and necessary effort aimed at modernizing how nonclinical safety fulfills its critical role in the development of safe medicines for our patients. Your work will help produce stable, reliable, and consistent AI-driven pipelines that accelerate our reporting timelines and build technology at the cutting edge of our field. The ideal candidate will have experience with the principles of Natural Language Processing (NLP) in addition to agent-based concepts including orchestration frameworks (such as LangChain, CrewAI, LlamaIndex, or equivalent), tool calling, and API integration.

Strong Python coding and documentation skills are essential. Familiarity with AWS and Google services, GitHub, LLMs, and vector databases is an additional advantage. Familiarity with nonclinical drug safety, report authoring, and summarization is a strong advantage. Key Responsibilities: Design and implement agentic workflows and orchestration pipelines to automate the retrieval, synthesis, and formatting of nonclinical safety data.

Develop robust data extraction pipelines in Python to parse and clean structured, semi-structured, and unstructured data from diverse scientific sources. Evaluate and benchmark LLM outputs for accuracy, formatting consistency, and alignment with strict nonclinical reporting templates. Collaborate closely with fellow data scientists and scientific SMEs to translate manual document-authoring steps into automated, step-by-step agent tools and skills.

Participate in agile sprints, contribute to team code reviews, and maintain clean, production-grade documentation via GitHub. Required Qualifications: Education: Currently pursuing a PhD in Computer Science, Data Science, Artificial Intelligence, Software Engineering, or a highly quantitative, research-focused field. Programming: Strong proficiency in Python programming with a focus on clean, modular, and well-documented code.

Data structures: Solid understanding of data structures and proficiency in manipulating structured, se