Senior Machine Learning Research Scientist

Pfizer • San Diego, CA

1d • $106,000 - $176,600 • Hybrid

About The Position

Pfizer is hiring a Senior Machine Learning Research Scientist to develop and advance machine learning methods that learn from and reason across complex biomedical, clinical, molecular, and scientific data. The role contributes to AI research intended to support drug discovery and development across therapeutic areas, including oncology, immunology and inflammation, internal medicine, and vaccines.

The scientist will develop, extend, and rigorously evaluate AI methods such as foundation models, multimodal and graph-based approaches, generative methods, and agentic reasoning systems. The work emphasizes reproducible experimentation, reusable research capabilities, rigorous evaluation, and evidence of scientific utility beyond model accuracy. The successful candidate will collaborate with AI researchers, computational biologists, engineers, clinical experts, and other domain specialists to translate research into trusted capabilities that can be applied across programs.

This is a hybrid role. Colleagues must live within commuting distance of a Pfizer location and work onsite an average of 2.5 days per week. The annual U.S. base salary range is $106,000 to $176,600, with eligibility for Pfizer’s Global Performance Plan (15% bonus target) and share-based long-term incentives. 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.

Requirements

• A PhD in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Applied Mathematics, Computational Biology, or a related quantitative field is 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 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 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 relevant area: 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 work authorization in the United States; U.S. work visa sponsorship is unavailable.

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 frontier AI methods for biomedical research, including foundation models, multimodal and graph-based approaches, generative methods, and agentic reasoning systems.
• Design and conduct clear, controlled, reproducible experiments to test hypotheses and compare competing approaches.
• Create datasets, benchmarks, and validation strategies to evaluate 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 scientific hypotheses and transparently interpret results, uncertainty, and limitations.
• Help translate research into trusted capabilities that can be deployed across discovery programs.
• Develop reusable methods, benchmarks, model components, and workflows; collaborate with engineering to run them on shared infrastructure and document, validate, and package them for reuse.
• Contribute to scientific reviews and research planning, mentor 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 15% bonus target.
• Eligibility to participate in a share-based long-term incentive program.
• Relocation assistance may be available based on business needs and/or eligibility.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior