Senior Machine Learning Research Scientist

• 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.

• 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.

The annual base salary for this position ranges from $106,000.00 to $176,600.00. In addition, this position is eligible for participation in Pfizers Global Performance Plan with a bonus target of 15.0% of the base salary and eligibility to participate in our share based long term incentive program. We offer comprehensive and generous benefits and programs to help our colleagues lead healthy lives and to support each of lifes moments. Benefits offered include a 401(k) plan with Pfizer Matching Contributions and an additional Pfizer Retirement Savings Contribution, paid vacation, holiday and personal days, paid caregiver/parental and medical leave, and health benefits to include medical, prescription drug, dental and vision coverage. Learn more at Pfizer Candidate Site U.S. Benefits | (uscandidates.mypfizerbenefits.com). Pfizer compensation structures and benefit packages are aligned based on the location of hire. The United States salary range provided does not apply to Tampa, FL or any location outside of the United States.

Relocation assistance may be available based on business needs and/or eligibility.

Candidates must be authorized to be employed in the U.S. by any employer.

U.S. work visa sponsorship (such as TN, O-1, H-1B, etc.) is not available for this role now or in the future.

Sunshine Act

Pfizer reports payments and other transfers of value to health care providers as required by federal and state transparency laws and implementing regulations. These laws and regulations require Pfizer to provide government agencies with information such as a health care providers name, address and the type of payments or other value received, generally for public disclosure. Subject to further legal review and statutory or regulatory clarification, which Pfizer intends to pursue, reimbursement of recruiting expenses for licensed physicians may constitute a reportable transfer of value under the federal transparency law commonly known as the Sunshine Act. Therefore, if you are a licensed physician who incurs recruiting expenses as a result of interviewing with Pfizer that we pay or reimburse, your name, address and the amount of payments made currently will be reported to the government. If you have questions regarding this matter, please do not hesitate to contact your Talent Acquisition representative.

At Pfizer, our purpose is to deliver breakthroughs that transform patients’ lives. Central to this mission is our Research and Development team, which strives to convert advanced science and cutting-edge technologies into impactful therapies and vaccines. Whether you are engaged in discovery sciences, ensuring drug safety and efficacy, or supporting clinical trials, your role is crucial. You will leverage innovative design and process development capabilities to expedite the delivery of top-tier medicines to patients globally., Pfizers 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. Positioned at the intersection of machine learning, protein science, and therapeutic discovery, the role is ideal for candidates eager to translate foundational AI research into real-world drug design. It offers the opportunity to join an innovative, collaborative environment that supports professional growth through mentorship, knowledge sharing, and the freedom to pursue impactful science.