2027 Future Talent Program – Translational Sciences and Outsourcing – Intern

Merck•South San Francisco, CA

12h•$39,108 - $111,111

About The Position

The Future Talent Program features internships that last up to 12 weeks 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. We are seeking a highly motivated intern student to join the Translational Sciences and Outsourcing (TSO) Data Science team within our Pharmacokinetics, Dynamics, Metabolism, and Bioanalytics (PDMB) department. The intern will contribute to the development and rigorous benchmarking of AI/ML methods for pharmacokinetic curve prediction, partnering with scientists to generate insights that support decision-making across drug discovery and development. The ideal candidate will combine strong machine learning implementation skills with scientific rigor, reproducible research practices, scientific curiosity, and a collaborative mindset, along with an interest in AI, pharmacokinetics, cheminformatics, and computational drug discovery. This internship provides an opportunity to apply modern AI/ML to large-scale pharmaceutical data, learn how computational methods are evaluated in an industrial research setting, and contribute to a reproducible, publication-oriented study with potential impact across the PDMB research portfolio.

Requirements

PhD student in computational chemistry, cheminformatics, machine learning, computer science, computational biology, statistics, or a related field.
A strong record of publication in top-tier, peer-reviewed scientific journals or machine learning conferences (e.g., ICML, ICLR, NeurIPS).
Strong Python programming skills, with experience using machine learning or deep learning frameworks such as PyTorch or similar tools. Experience with model development and evaluation.
Ability to work with complex scientific datasets, build reproducible analysis pipelines, and understand how data leakage and evaluation design affect model generalization.
Demonstrated interest in preparing a publishable computational drug discovery benchmark, with experience in designing comparative studies, building reproducible research workflows, and clearly interpreting and communicating technical results.
Strong communication skills and ability to summarize technical results clearly.

Nice To Haves

Experience with pharmacokinetics, pharmacometrics, ADMET, or drug discovery.
Familiarity with PK concepts such as concentration–time curves, AUC, Cmax, clearance, half-life, compartment models, and NCA.
Experience with molecular representations and cheminformatics tools, including SMILES, molecular fingerprints, graph neural networks, Chemprop, Uni-Mol, or RDKit; familiarity with molecular property prediction, QSAR, or ADME modeling is beneficial.
Experience with molecular similarity analysis, such as Tanimoto similarity, scaffold splitting, chemical series clustering, or out-of-distribution evaluation.
Experience with time-series modeling, ODE models, neural ODEs, or physics-informed machine learning.

Responsibilities

Analyze large-scale pharmacokinetic and molecular datasets and prepare them for reproducible modeling and evaluation.
Implement and benchmark classical machine learning baselines, molecular-structure-based models, and representative time-series or mechanism-informed approaches for pharmacokinetic curve prediction.
Design and compare random, scaffold, time-based, and chemical-similarity-aware evaluation settings, and quantify performance across compound novelty levels to identify data leakage and limitations in prospective generalization.
Collaborate with scientists to interpret results and ensure that benchmark conclusions are scientifically meaningful.
Document methods and results and contribute to a draft manuscript or technical report.
Present project outcomes at the end-of-summer intern symposium.