Senior Principal Machine Learning Scientist

Create Impact Beyond Research Papers

Publishing matters, but the goal is bigger. Your work will help scientists understand disease biology, prioritize experiments, and evaluate therapeutic opportunities across the portfolio. It will speed up medicines for the people waiting on them.

Work at the frontier, with the people who can ship it to patients.

You'll collaborate with AI researchers, computational biologists, engineers, and clinical and domain experts. Together you'll push state-of-the-art methods into real discovery programs, where the data is proprietary and the stakes are real.

Help invent how science gets done

We're building an AI-native operating model. Small, highly capable teams combine deep human expertise with AI agents and intelligent systems to move at a pace that once took far larger organizations. You'll help define it, prove it, and show others across R&D how to work this way. Every problem you solve should make the next one faster. You'll ask what generalizes, what's reusable, and what can run across programs. You'll build for transfer across diseases, modalities, data types, and teams.

The Opportunity

As a Senior Principal Machine Learning Scientist, you will develop and advance machine learning methods that learn from and reason across complex biomedical, clinical, molecular, and scientific data.

What You'll Do

Develop Frontier AI Methods for Biomedical Research. Create, adapt, and improve foundation models, multimodal and graph-based approaches, generative methods, and agentic reasoning systems. Go where conventional approaches fall short, and test whether what works in one therapeutic area works in the next.

Design and Execute Rigorous Experiments. Form clear hypotheses and design controlled, reproducible experiments. Compare competing approaches and find where they succeed, fail, or need rework. Let evidence drive decisions.

Set the Evaluation Standard At Scale. Design the datasets, benchmarks, and validation strategies that show whether a model can be trusted. Measure robustness, calibration, temporal and cohort generalization, cross-therapeutic transfer, reasoning ability, interpretability, and failure modes. Look past accuracy to real scientific utility.

Translate models into scientific evidence. Work with AI engineers, computational biologists, and domain experts to test hypotheses and interpret results, uncertainty, and limitations with transparency. Help promising research become trusted capabilities deployed across many programs.

Build the shared foundation. Develop reusable methods, benchmarks, model components, and workflows, and work with engineering to run them on common infrastructure. Document, validate, and package them so any team can apply the framework to a new disease area without starting over.

Raise the scientific bar. Contribute to scientific reviews and research planning. Mentor and learn from colleagues, and help raise AI fluency across R&D.

What You Bring

Education & research excellence

A PhD and 2+ years of 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

Proven Scientific Impact

You have demonstrated experience:

Developing, extending, or evaluating machine learning methodologies on hard, ambiguous, high value problems

Designing rigorous experiments and hypothesis-driven investigations

Building and comparing models using reproducible scientific approaches

Identifying limitations, biases, uncertainty, and failure modes

Translating research insights into meaningful outcomes and recommendations

Owning a research area, benchmark, model component, or scientific capability from concept through delivery

Technical & Research Proficiency

Strong Python and PyTorch, JAX or an equivalent framework

Model development, optimization, debugging and evaluation methodologies

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

Publications, patents, open-source contributions, deployed research, or other evidence of scientific impact are valued as indicators of expertise and execution.

How you work

Intellectual curiosity, sharp analytical reasoning, comfort with ambiguity, a habit of challenging assumptions (including your own), and the ability to influence through credibility and execution.

Nice to have

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

What Success Looks Like

You take ownership of important scientific questions and design experiments with rigor and purpose. You build things other teams pick up and use. A framework you create in one therapeutic area shows up in the next, and the one after that. You balance innovation with evidence, learn fast from failure, and influence people with data. You make the science around you better, and you stay focused on impact for patients.

Why Pfizer

At Pfizer, AI is becoming a foundational capability that helps power scientific discovery and accelerate the development of medicines and vaccines.

You'll have the opportunity to work with exceptional colleagues, unique scientific challenges, and rich biomedical data while helping shape the future of AI-enabled research.

Here, you'll find:

Meaningful ownership and scientific autonomy

Challenging, high-impact research problems backed by rich biomedical data

A chance to work with world leaders in medicine and scientific collaborators

Fastest path to growing your technical and scientific influence

The opportunity to build AI that improves patient outcomes worldwide

Work Location Assignment

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 for this position ranges from $139,100.00 to $231,900.00. In addition, this position is eligible for participation in Pfizer’s Global Performance Plan with a bonus target of 17.5% of the base salary and eligibility to participate in our share based long term incentive program. We offer comprehensive and generous benefits and programs to help our colleagues lead healthy lives and to support each of life’s moments. Benefits offered include a 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, and health benefits to include medical, prescription drug, dental and vision coverage.