Senior Machine Learning Research Scientist
Pfizer
Summary
Pfizer's Machine Learning and Computational Sciences (MLCS) group is seeking a Senior Machine Learning Scientist to lead independent and collaborative research in computational antibody design and optimization. This position focuses on developing and applying modern computational and machine-learning approaches to accelerate the discovery of next-generation biologic therapeutics.
Job description
What You Will Achieve
- 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 AI—particularly recent advances in deep learning—to 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 Pfizer's external visibility and reputation for scientific excellence through publications in high-impact journals and presentations at scientific conferences.
Minimum Requirements
- 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.
Preferred Requirements
- Prior experience or demonstrated interest in antibody design or protein engineering.
- Experience fine-tuning 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.
Requirements
- 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.
- Prior experience or demonstrated interest in antibody design or protein engineering.
- Experience fine-tuning 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.
Benefits
- 401(k) Matching
- Retirement Savings Contribution
- Paid Vacation
- Paid Holidays
- Paid Personal Days
- Paid Caregiver Leave
- Paid Parental Leave
- Paid Medical Leave
- Medical Insurance
- Prescription Drug Coverage
- Dental Insurance
- Vision Coverage
- Performance Bonus
- Long-Term Incentive Program
- Relocation Support
Company overview
Pfizer Inc. is an American multinational pharmaceutical and biotechnology corporation headquartered in New York City, founded in 1849 by German entrepreneurs Charles Pfizer and Charles F. Erhart. As one of the world's largest pharmaceutical companies, Pfizer develops and produces medications and vaccines across multiple therapeutic areas including immunology, oncology, cardiology, endocrinology, and neurology. The company is renowned for breakthrough drugs such as Lipitor, Viagra, and more recently, the Pfizer-BioNTech COVID-19 vaccine. With approximately 81,000 employees globally and operations in about 200 countries, Pfizer maintains 37 manufacturing sites worldwide. The company's current strategic focus includes oncology, rare diseases, and innovative vaccine development, while implementing significant cost-saving initiatives. Pfizer continues to advance its research pipeline with over 100 projects and maintains a strong commitment to scientific innovation and global healthcare access.