2027 Future Talent Program – Translational Sciences and Outsourcing – Intern

Merck · South San Francisco (grand Ave), CA

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Role summary

Merck seeks a PhD student intern for a 12-week summer program to benchmark machine learning methods for predicting pharmacokinetic profiles. The role involves analyzing large pharmaceutical datasets, implementing and comparing ML models, designing rigorous evaluation strategies, and contributing to a publishable benchmark study within the PDMB department.

What they're looking for

PhD student in computational chemistry, cheminformatics, machine learning, computer science, or related field
Strong publication record in top-tier peer-reviewed journals or ML conferences (ICML, ICLR, NeurIPS)
Advanced Python programming with PyTorch or similar ML/DL frameworks
Experience building reproducible analysis pipelines and understanding data leakage in model evaluation
Strong technical communication skills with ability to design and interpret comparative studies
Demonstrated interest in computational drug discovery benchmarking (preferred: pharmacokinetics, ADMET, or molecular representations)

What you'll be doing

Prepare and analyze large-scale pharmacokinetic and molecular datasets for reproducible modeling
Implement and benchmark classical ML, molecular-structure-based, and time-series models for PK curve prediction
Design and compare multiple evaluation strategies to assess model generalization and identify data leakage
Collaborate with scientists to interpret results and ensure scientific validity of findings
Document methods and results; contribute to draft manuscript and end-of-summer presentation

Compensation$39,108–$111,111

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