Senior Principal Machine Learning Scientist

Pfizer • Bothell, WA

2d • Hybrid

About The Position

Pfizer is hiring a Senior Principal Machine Learning Scientist to develop and advance AI methods for biomedical research and help establish scientific foundations for AI-enabled drug discovery and development. The role focuses on creating and rigorously evaluating reusable methods, models, benchmarks, evaluation frameworks, and research workflows that can generate scientific insight and transfer across therapeutic areas, data modalities, and teams.

The scientist will work with AI researchers, computational biologists, engineers, and clinical and other domain experts. The work spans foundation models, representation learning, multimodal and graph-based learning, generative methods, agentic AI, biological sequence and structure modeling, clinical and real-world data, uncertainty-aware AI, causal machine learning, and interpretability. The role requires evidence-driven research, with attention to robustness, calibration, generalization, interpretability, failure modes, and practical scientific utility. Research outputs should be documented, validated, packaged, and developed with engineering teams so they can be used across Pfizer R&D programs.

This position is hybrid. Colleagues must live within commuting distance of a Pfizer location and work on-site an average of 2.5 days per week. The annual base salary range is $139,100 to $231,900, with eligibility for a Global Performance Plan bonus target of 17.5% of base salary and a share-based long-term incentive program. Pfizer states that relocation assistance may be available based on business needs and/or eligibility. Candidates must be authorized to work in the U.S. by any employer; U.S. work visa sponsorship is not available for this role.

Requirements

• PhD and 2+ years of experience in machine learning, artificial intelligence, computer science, statistics, applied mathematics, computational biology, or a related quantitative field preferred; alternatively, a master's degree with 5+ years of relevant industry or research experience.
• Demonstrated experience developing, extending, or evaluating machine-learning methodologies on difficult, ambiguous, high-value problems.
• Experience designing rigorous experiments and hypothesis-driven investigations.
• Experience building and comparing models using reproducible scientific approaches.
• Ability to identify limitations, biases, uncertainty, and failure modes.
• Experience translating research insights into meaningful outcomes and recommendations.
• Experience owning a research area, benchmark, model component, or scientific capability from concept through delivery.
• Strong Python and experience with PyTorch, JAX, or an equivalent framework.
• Knowledge of model development, optimization, debugging, and evaluation methodologies.
• Knowledge of experimental design, statistical analysis, and reproducible research practices.
• Familiarity with scalable machine-learning workflows such as distributed training, experiment tracking, and shared evaluation pipelines.
• Depth in one or more of the following: multimodal learning, foundation models, graph neural networks, generative AI, agentic systems, biological sequence or structure modeling, knowledge graphs, clinical AI, omics and biomedical data, or scientific machine learning.
• Intellectual curiosity, analytical reasoning, comfort with ambiguity, willingness to challenge assumptions, and ability to influence through credibility and execution.
• Permanent U.S. work authorization; candidates must be authorized to work in the U.S. by any employer. Visa sponsorship is not available.

Nice To Haves

• Experience applying AI in life sciences, biotechnology, healthcare, pharmaceuticals, or biomedical research.
• Experience with biological, molecular, imaging, clinical, or real-world health data.
• Experience building foundation models or multimodal learning systems for scientific applications.
• Experience evaluating and implementing responsible AI methodologies.
• Experience working across multidisciplinary scientific organizations.
• Publications, patents, open-source contributions, deployed research, or other evidence of scientific impact.

Responsibilities

• Develop, adapt, and improve foundation models, multimodal and graph-based approaches, generative methods, and agentic reasoning systems for biomedical research.
• Test whether methods that work in one therapeutic area generalize to others.
• Form hypotheses and design controlled, reproducible experiments; compare approaches and use evidence to guide decisions.
• Design datasets, benchmarks, and validation strategies to assess model trustworthiness, robustness, calibration, temporal and cohort generalization, cross-therapeutic transfer, reasoning, interpretability, failure modes, and scientific utility.
• Work with AI engineers, computational biologists, and domain experts to test hypotheses and interpret results, uncertainty, and limitations transparently.
• Help translate promising research into trusted capabilities that can be deployed across programs.
• Develop reusable methods, benchmarks, model components, and workflows, and collaborate with engineering to run them on shared infrastructure.
• Document, validate, and package research tools and frameworks so teams can apply them to new disease areas.
• Contribute to scientific reviews and research planning.
• Mentor and learn from colleagues and help raise AI fluency across R&D.

Benefits

• 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.
• Medical, prescription drug, dental, and vision coverage.
• Eligibility for a Global Performance Plan bonus target of 17.5% of base salary.
• Eligibility to participate in a share-based long-term incentive program.
• Relocation assistance may be available based on business needs and/or eligibility.
• Comprehensive benefits and programs to support colleagues' health and life events.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree