Senior Scientist, Computational Biotherapeutics Engineering

Pfizer Pfizer · Pharma · MA

Senior Scientist role at Pfizer focused on computational AI/ML methods for biotherapeutics discovery and engineering. The role involves implementing, evaluating, and applying AI/ML models for protein modeling, representation learning, and generative design, with a focus on antibody developability and optimization. Requires expertise in Python, ML libraries, and biological datasets, with a strong understanding of protein structure and model evaluation.

What you'd actually do

  1. Implement advanced cutting-edge AI and machine learning workflows for computational protein design (including fine-tuning protein language models and generative protein design) in HPC or scalable cloud computing environments
  2. Collaborate with machine learning colleagues on the design and training of AI/ML models for antibody developability engineering. Apply these models to optimize leads for antibody drug discovery projects.
  3. Stay informed about developments in NLP, ML, and generative AI to create innovative solutions for molecular discovery, design, and optimization to advance therapeutic discovery and development.
  4. Serve as a technical expert in deep learning models for protein sequence and structure, supporting discovery teams with AI/ML‑driven design strategies.
  5. Analyze large‑scale sequence, structure, and experimental datasets to learn representations linking protein features to developability and pharmaceutical properties.

Skills

Required

  • PhD in biochemistry, computational chemistry, computational biology, machine learning, or a related field with 0 t 3 years of experience OR Master's Degree in biochemistry, computational chemistry, computational biology, machine learning with 7 to 8 years of experience OR BA/BS with 9 to 11 years of experience
  • Demonstrated track record (including publications or equivalent impact) of using AI/ML‑driven protein modeling/design to influence project direction and strategy
  • Hands‑on experience using and interrogating modern AI/ML models for protein representation, structure prediction, or generation (e.g., transformer or diffusion-based approaches).
  • Strong understanding of protein structure, sequence–structure relationships, and model evaluation.
  • 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 working with large biological datasets and bioinformatics resources.

Nice to have

  • Experience with protein language models (e.g., ESM‑family models), generative structure models (e.g., RFdiffusion, BoltzGen, BindCraft), and structural prediction AI models (e.g. AlphaFold)
  • Familiarity with equivariant or structure-aware neural networks.
  • Knowledge of antibody structure, multispecific design, or developability modeling.
  • Experience running and scaling deep learning workloads on HPC/GPU/cloud environments using technologies such as Slurm, AWS, or Google cloud.
  • Experience with structure-based molecular modeling software (Rosetta, Schrödinger, MOE, FoldX)

What the JD emphasized

  • Demonstrated track record (including publications or equivalent impact) of using AI/ML‑driven protein modeling/design to influence project direction and strategy
  • Hands‑on experience using and interrogating modern AI/ML models for protein representation, structure prediction, or generation (e.g., transformer or diffusion-based approaches).
  • Strong understanding of protein structure, sequence–structure relationships, and model evaluation.
  • Experience programming in Python and using modern scientific or machine learning libraries (e.g., NumPy/SciPy, scikit-learn, PyTorch), including training and evaluation workflows.

Other signals

  • implement advanced cutting-edge AI and machine learning workflows for computational protein design
  • collaborate with machine learning colleagues on the design and training of AI/ML models for antibody developability engineering
  • serve as a technical expert in deep learning models for protein sequence and structure
  • analyze large‑scale sequence, structure, and experimental datasets to learn representations linking protein features to developability and pharmaceutical properties