Senior Scientist, Computational Biotherapeutics Engineering
Pfizer
7 hours ago
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) in HPC or scalable cloud environments.
- Collaborate on designing/training AI/ML models for antibody developability engineering; apply to optimize leads for antibody drug discovery.
- Stay current on NLP, ML, and generative AI to create solutions for molecular discovery, design, and optimization.
- Serve as a technical expert on deep learning models for protein sequence/structure; support AI/ML-driven design strategies.
- Analyze large-scale sequence, structure, and experimental datasets to learn representations linking protein features 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 a computational developability platform.
Qualifications (Must-Have):
- PhD in biochemistry/computational chemistry/computational biology/ML (0–3 years) OR MS in these areas (7–8 years) OR BA/BS (9–11 years).
- Track record using AI/ML-driven protein modeling/design to influence strategy.
- Hands-on experience with modern AI/ML models for protein representation/structure prediction/generation (transformers or diffusion).
- Strong understanding of protein structure and sequence–structure relationships and model evaluation.
- Python programming and experience with NumPy/SciPy, scikit-learn, PyTorch; training/evaluation workflows.
- Experience with large biological datasets and bioinformatics resources.
Nice to Have:
- Protein language models; generative structure/prediction models (e.g., ESM, RFdiffusion, AlphaFold).
- Equivariant/structure-aware neural networks.
- Antibody structure/multispecific or developability modeling.
- Experience scaling deep learning on HPC/GPU/cloud (Slurm, AWS, Google Cloud).
- Structure-based molecular modeling tools (Rosetta, Schrödinger, MOE, FoldX).
Work Location: Hybrid (average 2.5 weekdays onsite/in-person).
Compensation/Benefits (as stated): Annual base salary $93,600–$156,000; bonus target 12.5%; eligibility for share-based long-term incentive; 401(k) with matching and retirement contribution; paid vacation/holidays/personal days; caregiver/parental and medical leave; health benefits (medical, prescription drug, dental, vision).
- Implement advanced AI/ML workflows for computational protein design (fine-tuning protein language models; generative protein design) in HPC or scalable cloud environments.
- Collaborate on designing/training AI/ML models for antibody developability engineering; apply to optimize leads for antibody drug discovery.
- Stay current on NLP, ML, and generative AI to create solutions for molecular discovery, design, and optimization.
- Serve as a technical expert on deep learning models for protein sequence/structure; support AI/ML-driven design strategies.
- Analyze large-scale sequence, structure, and experimental datasets to learn representations linking protein features 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 a computational developability platform.
Qualifications (Must-Have):
- PhD in biochemistry/computational chemistry/computational biology/ML (0–3 years) OR MS in these areas (7–8 years) OR BA/BS (9–11 years).
- Track record using AI/ML-driven protein modeling/design to influence strategy.
- Hands-on experience with modern AI/ML models for protein representation/structure prediction/generation (transformers or diffusion).
- Strong understanding of protein structure and sequence–structure relationships and model evaluation.
- Python programming and experience with NumPy/SciPy, scikit-learn, PyTorch; training/evaluation workflows.
- Experience with large biological datasets and bioinformatics resources.
Nice to Have:
- Protein language models; generative structure/prediction models (e.g., ESM, RFdiffusion, AlphaFold).
- Equivariant/structure-aware neural networks.
- Antibody structure/multispecific or developability modeling.
- Experience scaling deep learning on HPC/GPU/cloud (Slurm, AWS, Google Cloud).
- Structure-based molecular modeling tools (Rosetta, Schrödinger, MOE, FoldX).
Work Location: Hybrid (average 2.5 weekdays onsite/in-person).
Compensation/Benefits (as stated): Annual base salary $93,600–$156,000; bonus target 12.5%; eligibility for share-based long-term incentive; 401(k) with matching and retirement contribution; paid vacation/holidays/personal days; caregiver/parental and medical leave; health benefits (medical, prescription drug, dental, vision).