Principal Scientist, Bioinformatics

What you'll do

• Build agentic AI systems and predictive frameworks to identify and evaluate drug targets using multi-omic datasets, literature, and biological pathways.
• Develop generative models and geometric deep learning approaches to design specific binders and predict immune-interface interactions.
• Refine AI-designed binders and engineer their structural and biophysical properties for CAR-T applications.

What they ask for

• Ph.D. in Computational Biology, Computer Science, Structural Biology, or Biophysics.
• 3+ years of post-PhD experience, or 6+ years for MS/BS, in biotech/pharmaceutical industry or advanced research focused on computational drug discovery, target identification, and antibody/binder design.
• Expert programming proficiency in Python and deep learning frameworks, primarily PyTorch; experience building generative AI models and agentic AI systems using LLM orchestration frameworks.
• Deep familiarity with structural biology software, physics-based simulation and structural tools, structural immunology, interface biophysics, and multi-omics datasets and target evaluation databases.

Pay

$146,410 — $192,164 USD (base salary)

Tools

Python, PyTorch, AlphaFold, ESMFold, RFdiffusion, ProteinMPNN, BindCraft, FoldFlow

AI focus

Develops generative AI models and agentic AI systems for target discovery and binder design, including geometric deep learning for predicting paratope-epitope interactions.