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About the Role

At Pfizer, we believe the next generation of scientific breakthroughs will be powered by advances in artificial intelligence. As a Senior Principal Machine Learning Scientist, you will help create the scientific foundations behind that future at Pfizer, developing, extending, and rigorously evaluating advanced AI methods that generate new scientific insights, accelerate experimentation, and enable better decision-making across the drug discovery and development lifecycle.

Responsibilities

Develop Frontier AI Methods for Biomedical Research, including foundation models, multimodal and graph-based approaches, generative methods, and agentic reasoning systems.
Design and execute rigorous, controlled, and reproducible experiments to compare competing approaches.
Set the evaluation standard at scale by designing datasets, benchmarks, and validation strategies to measure robustness, calibration, generalization, reasoning ability, interpretability, and failure modes.
Translate models into scientific evidence by working with AI engineers, computational biologists, and domain experts to test hypotheses and interpret results.
Build the shared foundation by developing reusable methods, benchmarks, model components, and workflows.
Raise the Scientific Bar by contributing to scientific reviews and research planning, and mentoring colleagues.

Requirements

A PhD and 2+ years experience in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Applied Mathematics, Computational Biology, or a related quantitative field preferred.
A Master's degree with 5+ years of relevant industry or research experience.
Demonstrated experience developing, extending, or evaluating machine learning methodologies on hard, ambiguous, high value problems.
Demonstrated experience designing rigorous experiments and hypothesis-driven investigations.
Demonstrated experience building and comparing models using reproducible scientific approaches.
Demonstrated experience identifying limitations, biases, uncertainty, and failure modes.
Demonstrated experience translating research insights into meaningful outcomes and recommendations.
Demonstrated experience owning a research area, benchmark, model component, or scientific capability from concept through delivery.
Experience moving beyond experimentation alone to demonstrate measurable contributions to scientific progress, product capabilities, or business impact.
Strong Python and PyTorch, JAX or an equivalent framework.
Proficiency in model development, optimization, debugging and evaluation methodologies.
Proficiency in experimental design, statistical analysis and reproducible research practice.
Familiarity with scalable ML workflows, such as distributed training, experiment tracking, and shared evaluation pipelines.
Depth in one or more of: multimodal learning, foundation models, GNNs, generative AI, agentic systems, biological sequence or structure modeling, knowledge graphs, clinical AI, omics and biomedical data, scientific ML.
Intellectual curiosity, sharp analytical reasoning, comfort with ambiguity, a habit of challenging assumptions, and the ability to influence through credibility and execution.
Experience applying AI in life sciences, biotechnology, healthcare, pharmaceuticals, or biomedical research is a plus.
Experience with biological, molecular, imaging, clinical, or real-world health data is a plus.
Experience building foundation models or multimodal learning systems for scientific applications is a plus.
Experience evaluating and implementing responsible AI methodologies is a plus.
Experience working across multidisciplinary scientific organizations is a plus.
Permanent work authorization in the United States is required.
U.S. work visa sponsorship is not available for this role.

Skills

Machine Learning
Artificial Intelligence
Computer Science
Statistics
Applied Mathematics
Computational Biology
Python
PyTorch
JAX
Multimodal Learning
Foundation Models
Graph Neural Networks (GNNs)
Generative AI
Agentic Systems
Biological Sequence Modeling
Biological Structure Modeling
Knowledge Graphs
Clinical AI
Omics Data
Biomedical Data
Scientific Machine Learning
Distributed Training
Experiment Tracking
Shared Evaluation Pipelines
Responsible AI Methodologies

Location

Hybrid

Work Type

Hybrid
Full-time

Experience Level

Senior Principal
2+ years experience (with PhD)
5+ years relevant industry or research experience (with Master's)

Education Level

PhD preferred
Master's degree

Salary/Compensations

$139,100.00 - $231,900.00
Bonus target of 17.5% of base salary
Eligibility for share-based long-term incentive program

Benefits

401(k) plan with Pfizer Matching Contributions
Additional Pfizer Retirement Savings Contribution
Paid vacation
Holiday and personal days
Paid caregiver/parental and medical leave
Health benefits (medical, prescription drug, dental, and vision coverage)

About the Company

Frontier AI is changing what’s possible in science, and Pfizer is applying it to human health.
Pfizer has decades of world-class biology and chemistry, rich biomedical data and the global reach to get medicines to patients.
Pfizer is pairing its expertise with the newest advances in AI and working with world leaders in building medicine.
At Pfizer, AI is becoming a foundational capability that helps power scientific discovery and accelerate the development of medicines and vaccines.
Pfizer is committed to equal opportunity in employment.

Equal Opportunity

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.