Senior Machine Learning Research Scientist

Pfizer • Bothell, WA

2d • Hybrid

About The Position

Pfizer is seeking a Senior Machine Learning Research Scientist to develop and advance AI methods for biomedical, clinical, molecular, and scientific research. The role focuses on creating and rigorously evaluating reusable methods, models, benchmarks, and workflows that can generate scientific insights and support decision-making across drug discovery and development. The work is intended to generalize across diseases, modalities, data types, and research teams—not just solve isolated problems.

The scientist will work with AI researchers, computational biologists, engineers, and clinical and domain experts to test scientific hypotheses, interpret findings and limitations, and help translate promising research into capabilities used across Pfizer’s R&D programs. Research areas may include foundation models, multimodal learning, agentic AI and scientific reasoning, biological sequence and structure modeling, graph learning, clinical and real-world data modeling, uncertainty-aware AI, causal machine learning, and interpretability.

The 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 $106,000 to $176,600, with eligibility for a performance bonus and a share-based long-term incentive program. Relocation assistance may be available depending on business needs and eligibility. Candidates must have permanent U.S. work authorization; visa sponsorship is not available.

Requirements

• PhD in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Applied Mathematics, Computational Biology, or a related quantitative field preferred; alternatively, a Master’s degree with at least 2 years of relevant industry or research experience.
• Demonstrated experience developing, extending, or evaluating machine learning methodologies for 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 skills and experience with PyTorch, JAX, or an equivalent framework.
• Proficiency in 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 at least one area such as 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.
• Evidence of scientific impact, such as publications, patents, open-source contributions, or deployed research, is valued.
• Intellectual curiosity, analytical reasoning, comfort with ambiguity, a habit of challenging assumptions, and the ability to influence through credibility and execution.
• Must have permanent U.S. work authorization; U.S. work 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.

Responsibilities

• Develop, adapt, and improve machine learning methods for biomedical research, including foundation models, multimodal and graph-based approaches, generative methods, and agentic reasoning systems.
• Design and conduct controlled, reproducible experiments based on clear hypotheses; compare approaches and use evidence to assess their strengths and limitations.
• Develop datasets, benchmarks, and validation strategies to evaluate model reliability, robustness, calibration, generalization, transfer, reasoning, interpretability, and failure modes.
• Work with AI engineers, computational biologists, and domain experts to test hypotheses and interpret results, uncertainty, and limitations transparently.
• Develop reusable methods, benchmarks, model components, and workflows, and collaborate with engineering to run them on shared infrastructure.
• Document, validate, and package research capabilities 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, holidays, and personal days.
• Paid caregiver, parental, and medical leave.
• Medical, prescription drug, dental, and vision coverage.
• Eligibility to participate in Pfizer’s Global Performance Plan, with a bonus target of 15% of base salary.
• Eligibility to participate in a share-based long-term incentive program.
• Relocation assistance may be available based on business needs and eligibility.

What This Job Offers

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