Postdoctoral Fellow - AI/ML for Drug Metabolite Identification

As the world's leading pharmaceutical company, Pfizer is uniquely positioned with some of the largest and most complex chemical and biological data sets available anywhere. The Computational Absorption, Distribution, Metabolism, and Excretion (cADME) group at Pfizer is responsible for maximizing the value of ADME, Safety, and Pharmacology data generated to support drug discovery project teams. The Biotransformation group provides issue-driven drug metabolism expertise, including metabolite identification, structural elucidation, and interpretation of metabolic pathways, to support project teams across drug discovery and development.
We are seeking a highly motivated Postdoctoral Fellow with expertise in machine learning (ML) and artificial intelligence (AI) to develop predictive models for small-molecule drug metabolism. The role offers a unique opportunity to apply advanced ML/AI approaches to real-world pharmaceutical data at unprecedented scale, in close collaboration with experts in biotransformation, drug safety, and computational ADME. The fellow will have the opportunity to work with large-scale experimental metabolite identification (MetID) data, leveraging Pfizer's extensive, curated in vitro metabolism data assets to enable data-driven prediction of metabolic sites, metabolite structures, and transformation pathways.
This project aims to develop interpretable, mechanistically grounded ML models trained on empirical metabolite structure and abundance data and to integrate these predictive capabilities into AI-enabled decision-support workflows for drug discovery and development. This role will work closely with metabolism experts in real-time, testing and validating these models as experimental data is generated. The models developed in this project will be immediately impactful on internal decision making as well as influence the scientific field and regulatory landscape through external publication and application to ongoing evaluations.
Key Responsibilities
• Develop machine learning models to predict sites and types of small-molecule metabolism using large, curated MetID datasets.
Perform data engineering to represent metabolic reactions, structural changes, and atom-level involvement in biotransformation for efficient use in predictive models.
• Train, evaluate, and interpret ML models using experimentally derived metabolite structure and abundance of data.
• Collaborate closely with biotransformation, drug safety, and computational ADME scientists to ensure scientific relevance.
• Contribute to integration of predictive metabolism models into AI- and LLM-enabled decision-support workflows.
• Communicate results through internal presentations, cross-functional discussions, and scientific publications.
Required Qualifications
• PhD in a relevant discipline such as computational chemistry, cheminformatics, machine learning, data science, or a closely related field.
• Strong background in machine learning and/or artificial intelligence applied to large, complex scientific datasets.
• Experience working with chemical structure data and molecular representations or an interest in learning.
• Demonstrated ability to develop predictive models using empirical, experimentally derived data.
• Programming experience sufficient for large-scale data engineering and ML model development.
Preferred Qualifications
• Ability to work effectively in a highly collaborative, interdisciplinary research
environment.
• Strong written and verbal communication skills.
• Experience or familiarity with drug metabolism, biotransformation, MetID, or ADME-related data is advantageous.
• Interest in interpretable and mechanistically grounded ML approaches applied to drug discovery.
Additional Information

• Less than 2 years of post-degree experience.
• Willingness to make a minimum 2-year commitment.
• Successful record of scientific accomplishments evidenced by scientific publications and/or presentations with at least one first-author publication in a peer-reviewed journal.
• Two letters of recommendation are also required prior to interview stage.
• Relocation support available
• Work Location Assignment: Hybrid

The annual base salary for this position ranges from $64,600.00 to $107,600.00. In addition, this position is eligible for participation in Pfizer's Global Performance Plan with a bonus target of 7.5% of the base salary. We offer comprehensive and generous benefits and programs to help our colleagues lead healthy lives and to support each of life's 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.
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EEO & Employment Eligibility
Pfizer is committed to equal opportunity in the terms and conditions of employment for all employees and job applicants without regard to race, color, religion, sex, sexual orientation, age, gender identity or gender expression, national origin, disability or veteran status. Pfizer also complies with all applicable national, state and local laws governing nondiscrimination in employment as well as work authorization and employment eligibility verification requirements of the Immigration and Nationality Act and IRCA. Pfizer is an E-Verify employer. This position requires permanent work authorization in the United States.
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Research and Development