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# Senior Machine Learning Research Scientist

- **Company:** Pfizer Inc.
- **Location:** Cambridge, MA, United States
- **Experience:** Expert
- **Salary:** $106,000.0 - $176,600.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Computer Simulation, Software Debugging, Python (Programming Language), Machine Learning, Language Modeling, Molecular Modeling, NumPy, Scientific Computating, SciPy, Cloud Platform System, High Performance Computing, Pytorch, Scikit Learn, Information Technology, Slurm, Machine Learning Operations
- **Published:** October 17, 2026
- **Apply:** http://hbcu.com/cgi-bin/jobs/searchJobs.cgi?job_id=35276915

## About the Role

* Ph.D. in computational chemistry, the physical or biological sciences, chemical engineering, computer science, or a related discipline.
 * Familiarity with the concepts, techniques, and commonly used tools for sequence analysis and protein structure modeling.
 * Strong background in protein language models and structure-aware predictive and generative modeling.
 * Proficiency in Python and modern scientific computing and machine-learning libraries (e.g., NumPy, SciPy, scikit-learn, and PyTorch), including model training and evaluation workflows.
 * Experience with compute-intensive machine-learning workloads, including GPU acceleration, high-performance computing environments (e.g., Slurm), performance debugging, and reproducible experiment management.
 * Demonstrated ability to conduct independent research and produce publication-quality scientific work.

Bonus Points If You Have (Preferred Requirements):

 * Prior experience or demonstrated interest inantibody design or protein engineering.Experience finetuning foundation models using small and biased datasets.Experience training and deploying models in cloud environments (e.g., AWS, or GCP). Strong publication record and demonstrated contribution to the machine learning field.

## Description

* Conduct original research in computational antibody engineering, with an emphasis on sequence- and structure-based generative modeling.
 * Develop and deploy state-of-the-art machine-learning methods for multi-objective, constraint-aware antibody optimization that balances affinity, stability, and developability.
 * Apply proprietary computational frameworks and machine-learning models to predict antibody developability and affinity.
 * Leverage NGS-derived antibody repertoires to inform sequence- and structure-based rational design.
 * Collaborate with computational and experimental researchers across diverse scientific disciplines to identify challenges and opportunities and integrate computational chemistry, molecular modeling, and AIparticularly recent advances in deep learningto rapidly develop powerful computational solutions.
 * Communicate and explain computational models and machine-learning algorithms to broad scientific audiences across diverse disciplines.
 * Stay current with the relevant scientific literature and proactively identify, evaluate, and adopt promising external methods and tools.
 * Enhance Pfizers external visibility and reputation for scientific excellence through publications in high-impact journals and presentations at scientific conferences.