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Merck · 5 hours ago

Associate Scientist, Postdoctoral Fellow - Pharmacokinetics

Cambridge, MA

Full-time

Hybrid

New Grad, Entry Level

$70K/yr - $111K/yr

Merck is a leading pharmaceutical company seeking a highly motivated postdoctoral fellow with expertise in machine learning to help transform drug discovery and preclinical development. The role involves conducting original research, collaborating with interdisciplinary teams, and applying advanced machine learning methods to develop novel therapeutic candidates.

BiotechnologyHealth CareMedicalPharmaceutical

Comp. & Benefits

H1B Sponsored

Responsibilities

Conduct original research to develop state-of-the-art AI/Machine learning methods for drug discovery (e.g., molecular generative models, multi-objective optimization, property prediction, active learning, document authoring, document generation, hybrid AI system, multi-agent system)

Design and execute experiments, analyze results rigorously, and iterate rapidly on model architectures and training strategies

Build robust, reproducible code and workflows; contribute to shared libraries and documentation

Collaborate with chemists, biologists, Pharmacokinetics, Dynamics, Metabolism, and Bioanalytics (PDMB) scientists, and data/ML engineers to translate methods into impactful applications

Communicate findings through internal presentations and peer-reviewed publications; present at conferences and workshops

Qualification

Machine Learning (ML)PythonStatistical InferenceGenerative ModelsExperimental DesignPharmacokineticsInnovationCollaborationCommunicationProblem-Solving

Required

Ph.D. (or completion within six months) in Computer Science, Statistics, Physics, Applied Mathematics, Bioinformatics, Computational Biology, Chem/Informatics, Engineering, or a related field

Demonstrated research excellence and problem-solving ability; strong motivation to learn, innovate, and deliver

Proficiency in core ML/statistics topics such as probability, statistical inference, optimization, discrete math/algorithms, and/or probabilistic modeling

Strong programming skills in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow)

Track record of publications and/or presentations in ML, computational chemistry/biology, or related fields

Excellent collaboration and communication skills; proven ability to work in cross-functional teams

Preferred

Experience with molecular representations (e.g., SMILES, graphs), generative models (e.g., diffusion models, VAEs, flow models), and sequence/structure models (e.g., transformers, GNNs, protein or RNA models)

Familiarity with cheminformatics/biophysics toolkits (e.g., RDKit), docking or molecular simulation, ADMET modeling, or DMPK-relevant endpoints

Practical experience with experimental design, active learning, uncertainty quantification, or multi-objective optimization

Software engineering best practices (Git, testing, containers), and experience working with large datasets and cloud/GPU environment postdoctoralopportunities