Senior Principal Machine Learning Scientist

Pfizer • San Diego, CA

3d • $139,100 - $231,900 • Hybrid

About The Position

Pfizer is hiring a Senior Principal Machine Learning Scientist to develop and advance machine learning methods that learn from and reason across complex biomedical, clinical, molecular, and scientific data. The role focuses on creating and rigorously evaluating AI methods—including foundation models, multimodal and graph-based approaches, generative methods, and agentic reasoning systems—to generate scientific insights, accelerate experimentation, and improve decision-making throughout drug discovery and development.

The scientist will own model components, benchmarks, evaluation frameworks, and research workflows, assessing whether they deliver scientific value and generalize across therapeutic areas, data types, and programs. The role combines research with practical translation: collaborating with AI researchers, computational biologists, engineers, clinicians, and other domain experts to test hypotheses, interpret results and limitations, and help turn promising research into trusted capabilities used across Pfizer R&D. The scientist will also develop reusable methods and shared resources, contribute to research planning and scientific reviews, mentor colleagues, and help advance AI fluency across R&D.

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 stated U.S. annual base salary range is $139,100 to $231,900, with eligibility for a performance-plan bonus 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.; U.S. work visa sponsorship is unavailable.

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 skills and proficiency 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 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, strong analytical reasoning, comfort with ambiguity, a habit of challenging assumptions, and the 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.

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 are valued as indicators of expertise and execution.

Responsibilities

• Develop, adapt, and improve frontier AI methods for biomedical research, including foundation models, multimodal and graph-based approaches, generative methods, and agentic reasoning systems.
• Design and execute hypothesis-driven, controlled, reproducible experiments; compare approaches and use evidence to assess their performance and limitations.
• Create datasets, benchmarks, and validation strategies that evaluate robustness, calibration, temporal and cohort generalization, cross-therapeutic transfer, reasoning, interpretability, failure modes, and scientific utility.
• Collaborate with AI engineers, computational biologists, and domain experts to test scientific hypotheses and transparently interpret results, uncertainty, and limitations.
• Translate promising research into trusted capabilities that can be used across multiple research programs.
• Develop reusable methods, benchmarks, model components, and workflows; work with engineering to run them on shared infrastructure and document, validate, and package them for reuse.
• 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 to participate in Pfizer’s Global Performance Plan, with a 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 intended to support colleagues’ health and life needs.

What This Job Offers

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