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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Research Scientist - **Company:** Pfizer Inc. - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $106,000 - $176,600 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Computational Biology, Software Debugging, Distributed Computing Environment, Graph Database, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Graph Neural Networks, Multimodal Learning, Pytorch, Evaluation Pipelines, Large Language Models, Generative AI, Agentic-AI, Information Technology, Free and Open-Source Software, Machine Learning Operations, Feature Extraction - **Published:** November 8, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-machine-learning-research-scientist-new-york-ny--361a861a-edab-4ae0-a39a-bbd821dd9c5f ## About the Role Machine Learning, Scientific Research, Medicine, Artificial Intelligence (AI), Human Health, Biology, Chemistry, Biomedicine, Medical Products, Machine Learning, * A PhD in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Applied Mathematics, Computational Biology, or a related quantitative field preferred * A Master's degree with 2+ 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 We are looking for scientists who have moved beyond experimentation alone and can demonstrate measurable contributions to scientific progress, product capabilities, or business impact. Technical & Research Proficiency * Strong Python and PyTorch, JAX or an equivalent framework * Model development, optimization, debugging and evaluation methodologiesExperimental 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 ## Description 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 Machine Learning Research 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.