AI Cheminformatics Scientist at Pfizer applying AI methods to drug discovery. Collaborating with medicinal chemists and biologists while developing cheminformatics workflows.
About the role
- AI/ML Summer Intern working with Statistics & Decision Sciences at Johnson & Johnson. Engaging in real-world problems in pharmaceutical R&D and manufacturing over the summer.
Responsibilities
- During the internship you’ll be assigned to one or more of the following focus areas (we’ll match based on your skills and interests):
- 1) AI for Scientific & Process Modeling Design and prototype agentic AI workflows that discover, select, and fit mathematical models (e.g., dissolution profiles; broader process/kinetics use cases). Build and benchmark nonlinear curve-fitting and optimization routines; define quality/fit criteria and validation protocols. Generalize methods to additional pharma processes (stability modeling, process optimization, PK/PD signals). Package your work into reusable components and documentation for scientist end-users.
- 2) LLM Platform Integration for R Analytics Help enable secure, enterprise LLM capabilities for R-based statistical workflows by developing and testing OpenAI-compatible API endpoints for a self-hosted LLM stack. Implement and validate OpenAI-style /v1/chat/completions endpoints; support streaming and non-streaming modes. Add secure authentication, configuration for multiple models, and enterprise logging/guardrails. Create test suites and integration examples with R packages (e.g., ellmer, vitals); contribute to documentation and deployment guides; plan for future RAG/embedding integration.
Requirements
- Enrolled in an accredited European university (Bachelor’s, Master’s, or PhD) in Computer Science, Data Science, Statistics, Applied Mathematics, or a related field; available full-time 10–12 weeks between June 1 and Sept 30, 2026.
- Strong Python skills (scientific stack: NumPy/SciPy/pandas) and sound software development practices with Git.
- Solid grounding in statistical modeling, regression, and optimization; ability to analyze noisy experimental data.
- Experience with machine learning concepts and modern LLM usage patterns/APIs.
- Clear, proactive communicator; able to work independently and in cross-functional teams.
- Legally authorized to work in the hiring country without current or future visa sponsorship.
- Experience with R and the analytical ecosystem (e.g., ellmer, testthat, shiny) (nice to have)
- Familiarity with OpenAI-compatible endpoints, FastAPI, microservices, and REST testing (nice to have)
- Knowledge of vector databases, embeddings/RAG, and secure logging/guardrails in regulated settings (nice to have)
- Exposure to Bayesian methods, uncertainty quantification, or PK/PD/process modeling (nice to have)
- Cloud/containerization familiarity (AWS/Azure/GCP, Docker) for scalable deployments (nice to have)
Benefits
- Practical experience applying AI/ML to real pharmaceutical problems in R&D and manufacturing
- Mentorship from senior statisticians/engineers and opportunities to present your work to stakeholders
- A tangible portfolio: prototypes, APIs, tests, and documentation that can be adopted by end users
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