Postdoctoral Fellow Generative AI for Antibody Engineering
Pfizer
6 months ago
On-site
Cambridge, MA
Clinical Research and Development
Role Summary
- Conduct independent and collaborative research in computational antibody design and optimization.
- Develop and apply modern computational and machine-learning approaches to accelerate discovery of next-generation biologic therapeutics.
What You Will Achieve
- Conduct original research in computational antibody engineering using sequence- and structure-based generative modeling.
- Develop and deploy state-of-the-art ML methods for multi-objective, constraint-aware antibody optimization (affinity, stability, developability).
- Apply proprietary computational frameworks and ML models for antibody developability engineering.
- Communicate findings through manuscripts, conference presentations, and internal seminars.
- Collaborate with computational and experimental researchers in a multidisciplinary environment.
Here Is What You Need (Minimum Requirements)
- Ph.D. in computational chemistry, physical or biological sciences, chemical engineering, computer science, or related field.
- Less than 2 years of post-degree experience.
- Willingness to make a minimum 2-year commitment.
- Record of scientific accomplishments (publications/presentations), including at least one first-author peer-reviewed journal publication.
- Two letters of recommendation required prior to interview.
- Strong background in protein language models and structure-aware generative models.
- Python programming experience; modern ML/scientific libraries (NumPy/SciPy, scikit-learn, PyTorch) and training/evaluation workflows.
- Experience with reinforcement learning, Bayesian optimization, or other advanced multi-objective optimization.
- Experience with compute-intensive ML (GPU, HPC e.g., Slurm), performance debugging, and reproducible experiment management.
- Ability to conduct independent research and produce publishable work.
Bonus Points If You Have (Preferred Requirements)
- Experience or interest in antibody design/protein engineering.
- Experience fine-tuning foundation models on small/biased datasets.
- Familiarity with antibody developability properties.
- Cloud ML experience (AWS/GCP), Docker, Kubernetes, and basic MLOps (versioning, CI/CD, monitoring).
Additional Information / Application
- Work location: On Premise
- Last day to apply: April 30, 2026
Benefits (if applicable)
- Eligible for Pfizer Global Performance Plan bonus target of 7.5% of base salary.
- Comprehensive benefits including 401(k) with matching contributions, additional retirement savings contribution, paid vacation/holidays/personal days, paid caregiver/parental and medical leave, and medical/prescription drug/dental/vision coverage.
Compensation
- Annual base salary range: $64,600.00 to $107,600.00.
- Conduct independent and collaborative research in computational antibody design and optimization.
- Develop and apply modern computational and machine-learning approaches to accelerate discovery of next-generation biologic therapeutics.
What You Will Achieve
- Conduct original research in computational antibody engineering using sequence- and structure-based generative modeling.
- Develop and deploy state-of-the-art ML methods for multi-objective, constraint-aware antibody optimization (affinity, stability, developability).
- Apply proprietary computational frameworks and ML models for antibody developability engineering.
- Communicate findings through manuscripts, conference presentations, and internal seminars.
- Collaborate with computational and experimental researchers in a multidisciplinary environment.
Here Is What You Need (Minimum Requirements)
- Ph.D. in computational chemistry, physical or biological sciences, chemical engineering, computer science, or related field.
- Less than 2 years of post-degree experience.
- Willingness to make a minimum 2-year commitment.
- Record of scientific accomplishments (publications/presentations), including at least one first-author peer-reviewed journal publication.
- Two letters of recommendation required prior to interview.
- Strong background in protein language models and structure-aware generative models.
- Python programming experience; modern ML/scientific libraries (NumPy/SciPy, scikit-learn, PyTorch) and training/evaluation workflows.
- Experience with reinforcement learning, Bayesian optimization, or other advanced multi-objective optimization.
- Experience with compute-intensive ML (GPU, HPC e.g., Slurm), performance debugging, and reproducible experiment management.
- Ability to conduct independent research and produce publishable work.
Bonus Points If You Have (Preferred Requirements)
- Experience or interest in antibody design/protein engineering.
- Experience fine-tuning foundation models on small/biased datasets.
- Familiarity with antibody developability properties.
- Cloud ML experience (AWS/GCP), Docker, Kubernetes, and basic MLOps (versioning, CI/CD, monitoring).
Additional Information / Application
- Work location: On Premise
- Last day to apply: April 30, 2026
Benefits (if applicable)
- Eligible for Pfizer Global Performance Plan bonus target of 7.5% of base salary.
- Comprehensive benefits including 401(k) with matching contributions, additional retirement savings contribution, paid vacation/holidays/personal days, paid caregiver/parental and medical leave, and medical/prescription drug/dental/vision coverage.
Compensation
- Annual base salary range: $64,600.00 to $107,600.00.