Senior Scientist, Computational Biotherapeutics Engineering
Pfizer
August 10, 2026
Remote friendly (Cambridge, MA)
United States
Clinical Research and Development
Responsibilities:
- Implement advanced AI/ML workflows for computational protein design (fine-tuning protein language models; generative protein design) on HPC or scalable cloud.
- Collaborate on ML model design/training for antibody developability; apply models to optimize leads for antibody drug discovery.
- Stay current on NLP/ML/generative AI to enable molecular discovery, design, and optimization.
- Serve as a technical expert in deep learning for protein sequence/structure to support AI/ML-driven design strategies.
- Analyze large-scale sequence/structure/experimental datasets to learn representations linked to developability and pharmaceutical properties.
- Communicate model behavior, limitations, and design recommendations to technical and non-technical audiences.
- Collaborate with computational and wet lab experts to optimize the computational developability platform.
Qualifications (Must-Have):
- PhD (0–3 years) in biochemistry/computational chemistry/computational biology/ML (or related); or MS (7–8 years); or BA/BS (9–11 years).
- Track record (publications/equivalent impact) using AI/ML-driven protein modeling/design to influence strategy.
- Hands-on experience with modern AI/ML for protein representation, structure prediction, or generation (e.g., transformers/diffusion).
- Strong knowledge of protein structure and sequence–structure relationships and model evaluation.
- Python programming; modern ML/scientific libraries (NumPy/SciPy, scikit-learn, PyTorch) and training/evaluation workflows.
- Experience with large biological datasets/bioinformatics resources.
Nice to Have:
- Protein language models (e.g., ESM), generative structure models (e.g., RFdiffusion, BoltzGen, BindCraft), AlphaFold.
- Equivariant/structure-aware neural networks; antibody/multispecific/developability modeling.
- HPC/GPU/cloud scaling (Slurm, AWS, Google Cloud); structure-based modeling tools (Rosetta, Schrödinger, MOE, FoldX).
Work Location: Hybrid (onsite/in-person ~2.5 weekdays/week).
Compensation/Benefits: Base salary $93,600–$156,000; bonus target 12.5%; 401(k) with matching; retirement contribution; paid vacation/holidays; caregiver/parental and medical leave; medical/dental/vision.
- Implement advanced AI/ML workflows for computational protein design (fine-tuning protein language models; generative protein design) on HPC or scalable cloud.
- Collaborate on ML model design/training for antibody developability; apply models to optimize leads for antibody drug discovery.
- Stay current on NLP/ML/generative AI to enable molecular discovery, design, and optimization.
- Serve as a technical expert in deep learning for protein sequence/structure to support AI/ML-driven design strategies.
- Analyze large-scale sequence/structure/experimental datasets to learn representations linked to developability and pharmaceutical properties.
- Communicate model behavior, limitations, and design recommendations to technical and non-technical audiences.
- Collaborate with computational and wet lab experts to optimize the computational developability platform.
Qualifications (Must-Have):
- PhD (0–3 years) in biochemistry/computational chemistry/computational biology/ML (or related); or MS (7–8 years); or BA/BS (9–11 years).
- Track record (publications/equivalent impact) using AI/ML-driven protein modeling/design to influence strategy.
- Hands-on experience with modern AI/ML for protein representation, structure prediction, or generation (e.g., transformers/diffusion).
- Strong knowledge of protein structure and sequence–structure relationships and model evaluation.
- Python programming; modern ML/scientific libraries (NumPy/SciPy, scikit-learn, PyTorch) and training/evaluation workflows.
- Experience with large biological datasets/bioinformatics resources.
Nice to Have:
- Protein language models (e.g., ESM), generative structure models (e.g., RFdiffusion, BoltzGen, BindCraft), AlphaFold.
- Equivariant/structure-aware neural networks; antibody/multispecific/developability modeling.
- HPC/GPU/cloud scaling (Slurm, AWS, Google Cloud); structure-based modeling tools (Rosetta, Schrödinger, MOE, FoldX).
Work Location: Hybrid (onsite/in-person ~2.5 weekdays/week).
Compensation/Benefits: Base salary $93,600–$156,000; bonus target 12.5%; 401(k) with matching; retirement contribution; paid vacation/holidays; caregiver/parental and medical leave; medical/dental/vision.