Postdoctoral Fellow Generative AI for Antibody Engineering Full-time

Postdoctoral Scholar In Computational Antibody Design And Optimization

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.

Pfizer's Machine Learning Computational Sciences (MLCS) group is seeking a highly motivated postdoctoral scholar to conduct independent and collaborative research in computational antibody design and optimization. This position focuses on the development and application of modern computational and machine-learning–based approaches to accelerate the discovery of next-generation biologic therapeutics. The role sits at the intersection of machine learning, protein science, and therapeutic discovery, ideal for candidates eager to translate foundational AI research into real-world drug design while supporting scientific publication and professional development. The selected postdoc will join a cohort of other AI-focused postdoctoral researchers working on a variety of R&D topics. This community offers opportunities for peer mentorship and exposure to diverse applications of machine learning in discovery and development.

What You Will Achieve

• Conduct original research in computational antibody engineering, with an emphasis on sequence- and structure-based generative modeling.
• Develop and deploy state-of-the-art ML methods for multi-objective, constraint-aware antibody optimization balancing affinity, stability and developability.
• Apply proprietary computational framework and ML models for antibody developability engineering.
• Communicate research findings through manuscripts, conference presentations, and internal seminars.
• Collaborate with computational and experimental researchers in a multidisciplinary research environment.

Here Is What You Need (Minimum Requirements):

• Ph.D. degree in computational chemistry, physical or biological sciences, chemical engineering, computer science, or related discipline.
• Less than 2 years of post-degree experience.
• Willingness to make a minimum 2-year commitment.
• Successful record of scientific accomplishments evidenced by scientific publications and/or presentations with at least one first-author publication in a peer-reviewed journal.
• Two letters of recommendation are also required prior to interview stage.
• Strong background in protein language models and structure-aware generative models.
• Experience programming in Python and using modern scientific or machine learning libraries (e.g., NumPy/SciPy, scikit-learn, PyTorch), including training and evaluation workflows.
• Experience with reinforcement learning, Bayesian optimization, or other advanced multi-objective optimization techniques.
• Experience with compute-intensive ML workloads, including GPU acceleration and HPC environments (e.g., Slurm), performance debugging, and reproducible experiment management.
• Demonstrated ability to conduct independent research and produce publishable 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.
• Familiarity with antibody developability properties is highly desirable.
• Experience training and deploying models in cloud environments (e.g., AWS, or GCP), including containers (Docker), orchestration (Kubernetes), and basic MLOps practices (versioning, CI/CD, monitoring).

Additional Information

• Work Location Assignment: On Premise
• Last day to apply April 30th, 2026