Senior Principal Machine Learning Scientist

Pfizer

. Develop, extend, and rigorously evaluate advanced AI methods for biomedical research and drug discovery .

Posted 11/6/2026full-timeUnited StatesSenior💰 $139,100 - $231,900 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Expertise in developing and evaluating advanced AI methodologies for biomedical research, with a strong focus on experimental design, model optimization, and translating research insights into actionable outcomes. Proven ability to collaborate with interdisciplinary teams and mentor colleagues to enhance AI fluency across research and development.

Highest-signal resume keywords

PhD In Machine Learning Or Related FieldStrong Python And PyTorchExperimental Design And Statistical AnalysisModel Development And Evaluation MethodologiesExperience With Multimodal Learning And Generative AI

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Machine LearningArtificial IntelligenceStatistical AnalysisExperimental DesignModel OptimizationReproducible Research PracticeHypothesis-Driven InvestigationsModel DebuggingEvaluation MethodologiesData Benchmarking

Soft Skills

CollaborationMentoringCommunicationProblem-SolvingInterdisciplinary Teamwork

Tools & Technologies

PyTorchJAXDistributed TrainingExperiment TrackingShared Evaluation Pipelines

Industry Keywords

Biomedical ResearchDrug DiscoveryGenerative AIMultimodal LearningGraph-Based ApproachesAgentic Reasoning SystemsClinical AIOmics DataScientific MLKnowledge Graphs

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

Develop, extend, and rigorously evaluate advanced AI methods for biomedical research and drug discovery
Create, adapt, and improve foundation models, multimodal and graph-based approaches, generative methods, and agentic reasoning systems
Design clear hypotheses and execute controlled, reproducible experiments
Compare competing approaches and identify where they succeed, fail, or need rework
Design datasets, benchmarks, and validation strategies for model trustworthiness
Measure robustness, calibration, temporal and cohort generalization, cross-therapeutic transfer, reasoning ability, interpretability, and failure modes
Translate models into scientific evidence with AI engineers, computational biologists, and domain experts
Develop reusable methods, benchmarks, model components, and workflows on common infrastructure
Document, validate, and package frameworks for reuse across disease areas and teams
Contribute to scientific reviews and research planning
Mentor colleagues and raise AI fluency across R&D
Collaborate with AI researchers, computational biologists, engineers, and clinical and domain experts to advance drug discovery and development

Requirements

What you’ll need

PhD and 2+ years experience in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Applied Mathematics, Computational Biology, or a related quantitative field preferred
Master's degree with 5+ years of relevant industry or research experience
Demonstrated experience developing, extending, or evaluating machine learning methodologies on hard, ambiguous, high-value problems
Experience designing rigorous experiments and hypothesis-driven investigations
Experience building and comparing models using reproducible scientific approaches
Experience identifying 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 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, including 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, or scientific ML
Publications, patents, open-source contributions, deployed research, or other evidence of scientific impact valued as indicators of expertise and execution
Candidates must be authorized to be employed in the U.S. by any employer
Permanent work authorization in the United States required
U.S. work visa sponsorship is not available now or in the future

Benefits

Comp & perks

Global Performance Plan with a bonus target of 17.5% of base salary
Share based long term incentive program
401(k) plan with Pfizer Matching Contributions
Additional Pfizer Retirement Savings Contribution
Paid vacation, holiday and personal days
Paid caregiver/parental and medical leave
Medical, prescription drug, dental and vision coverage
Relocation assistance may be available based on business needs and/or eligibility
Hybrid work arrangement