# Senior Machine Learning Research Scientist at Pfizer

Pfizer is hiring a Senior Machine Learning Research Scientist in Cambridge, United States. It pays $106k-$177k a year and Level rates it Builds AI ●●●●; you can [apply on Level](https://jobsbylevel.com/go/f040c54a-1a65-4343-bfea-4cbb4c7a7494?utm_medium=ai-agent).

AI Level 4, AI centrality 94 out of 100. United States - Massachusetts - Cambridge.

## Details

- Company: [Pfizer](https://jobsbylevel.com/companies/pfizer)
- AI level: AI Level 4 (score 94 out of 100)
- Location: United States - Massachusetts - Cambridge
- Salary: $106k-$177k
- Posted: October 15, 2026
- Apply: https://jobsbylevel.com/go/f040c54a-1a65-4343-bfea-4cbb4c7a7494?utm_medium=ai-agent

## Prepare for this job

The job page lists 5 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/senior-machine-learning-research-scientist-at-pfizer-440926

## Description

Use Your Power for Purpose At Pfizer, our purpose is to deliver breakthroughs that transform patients' lives. Central to this mission is our Research and Development team, which strives to convert advanced science and cutting-edge technologies into impactful therapies and vaccines. Whether you are engaged in discovery sciences, ensuring drug safety and efficacy, or supporting clinical trials, your role is crucial. You will leverage innovative design and process development capabilities to expedite the delivery of top-tier medicines to patients globally. Role Summary Pfizer’s Machine Learning and Computational Sciences (MLCS) group is seeking a Senior Machine Learning Scientist to lead independent and collaborative research in computational antibody design and optimization. This position focuses on developing and applying modern computational and machine-learning approaches to accelerate the discovery of next-generation biologic therapeutics. Positioned at the intersection of machine learning, protein science, and therapeutic discovery, the role is ideal for candidates eager to translate foundational AI research into real-world drug design. It offers the opportunity to join an innovative, collaborative environment that supports professional growth through mentorship, knowledge sharing, and the freedom to pursue impactful science. What You Will Achieve In this role, you will: Conduct original research in computational antibody engineering, with an emphasis on sequence- and structure-based generative modeling. Develop and deploy state-of-the-art machine-learning methods for multi-objective, constraint-aware antibody optimization that balances affinity, stability, and developability. Apply proprietary computational frameworks and machine-learning models to predict antibody developability and affinity. Leverage NGS-derived antibody repertoires to inform sequence- and structure-based rational design. Collaborate with computational and experimental researchers across diverse scientific disciplines to identify challenges and opportunities and integrate computational chemistry, molecular modeling, and AI—particularly recent advances in deep learning—to rapidly develop powerful computational solutions. Communicate and explain computational models and machine-learning algorithms to broad scientific audiences across diverse disciplines. Stay current with the relevant scientific literature and proactively identify, evaluate, and adopt promising external methods and tools. Enhance Pfizer’s external visibility and reputation for scientific excellence through publications in high-impact journals and presentations at scientific conferences. Here Is What You Need (Minimum Requirements): Ph.D. in computational chemistry, the physical or biological sciences, chemical engineering, computer science, or a related discipline. Familiarity with the concepts, techniques, and commonly used tools for sequence analysis and protein structure modeling. Strong background in protein language models and structure-aware predictive and generative modeling. Proficiency in Python and modern scientific computing and machine-learning libraries (e.g., NumPy, SciPy, scikit-learn, and PyTorch), including model training and evaluation workflows. Experience with compute-intensive machine-learning workloads, including GPU acceleration, high-performance computing environments (e.g., Slurm), performance debugging, and reproducible experiment management. Demonstrated ability to conduct independent research and produce publication-quality scientific work. Bonus Points If You Have (Preferred Requirements): Prior experience or demonstrated interest in antibody design or protein engineering.Experience fine‑tuning foundation models using small and biased datasets.Experience training and deploying models in cloud environments (e.g., AWS, or GCP). Strong publication record and demonstrated contribution to the machine learning field. Additional Information: Work Location Assignment:

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