Senior Principal AI Scientist — Biomedical Discovery

Aimlroles; Washington; Hybrid; USD 139,000 - 232,000; Full time

Benefits offered by this job

401(k) with Pfizer matching

Paid vacation

Comprehensive medical, dental and life

Job summary

Pfizer is seeking a Senior Principal Machine Learning Scientist to develop frontier AI methods for biomedical research, including foundation models, multimodal and graph-based approaches, and agentic reasoning systems.

You will design rigorous experiments, set scalable evaluation standards, and translate results into actionable scientific evidence deployed across drug discovery and development programs.

Qualifications

• PhD and 2+ years of experience in Machine Learning, AI, CS, Statistics, Applied Mathematics, Computational Biology, or related field preferred.
• Master’s degree with 5+ years of relevant industry or research experience.

Responsibilities

• Develop frontier AI methods for biomedical research across foundation models, multimodal and graph-based approaches, generative methods, and agentic reasoning systems.
• Design and execute rigorous experiments with clear hypotheses and reproducible methods.
• Set the evaluation standard at scale by designing datasets, benchmarks, and validation strategies.
• Translate research results into scientific evidence and trusted capabilities deployed across programs.
• Build reusable frameworks and model components to enable transfer across disease areas and data modalities.
• Raise the scientific bar by contributing to reviews, planning, and mentoring colleagues.

Skills

Python

PyTorch

JAX

Experiment design

Statistical analysis

Education

PhD in ML or related field

Tools

Foundation models

Multimodal learning

GNNs

Biomedical data

Job description

Pfizer is seeking a Senior Principal Machine Learning Scientist to develop frontier AI methods for biomedical research, including foundation models, multimodal and graph-based approaches, and agentic reasoning systems.

You will design rigorous experiments, set scalable evaluation standards, and translate results into actionable scientific evidence deployed across drug discovery and development programs.