Senior Principal Machine Learning Scientist

Build AI That Could Help Transform the Future of Medicine

Frontier AI is changing what’s possible in science, and few places let you apply it to a problem as consequential as human health. Pfizer is one of them. We have decades of world-class biology and chemistry, rich biomedical data and the global reach to get medicines to patients. We are pairing them with the newest advances in AI and we are doing it along with the world’s leaders in building medicine.

At Pfizer, we believe the next generation of scientific breakthroughs will be powered by advances in artificial intelligence. From foundation models and multimodal learning to biological reasoning and agentic systems, AI has the potential to fundamentally change how we discover, develop, and deliver medicines to patients. As a Senior Principal Machine Learning Scientist, you will help create the scientific foundations behind that future at Pfizer.

We are not looking for one clever model for one clever problem or applying existing models to a single problem. You will develop, extend, and rigorously evaluate advanced AI methods that generate new scientific insights, accelerate experimentation, and enable better decision-making across the drug discovery and development lifecycle. We are building AI that compounds. A method, benchmark or model that works in one disease area should become a foundation that can impact across oncology, immunology/inflammation, internal medicine, vaccines and beyond. You’ll design for that!

Working alongside world-class AI researchers, computational biologists, engineers, and domain experts, you'll tackle some of the most challenging and meaningful problems in healthcare while building technology that has the potential to impact millions of patients worldwide.

Why This Role Is Different

Own Meaningful Scientific Challenges: You will own defined model components, benchmarks, evaluation frameworks, and research workflows that directly influence how AI is applied across Pfizer's R&D organization. You'll build the models, and you'll also decide whether they create real scientific value and whether that value holds up in new contexts.

Where you will push the frontier: You'll contribute to cutting-edge research across areas such as:

• Foundation models and representation learning.
• Multimodal learning across molecular, imaging, clinical and omics data
• Agentic AI and scientific reasoning systems
• Biological Sequence & Structure Modeling
• Graph learning and knowledge graphs
• Clinical & Real-World Data Modeling
• Uncertainty-Aware AI, causal ML and Interpretability

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.

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