Tech Stack

Description

You will lead the computational strategy for engineering large serine recombinases (LSRs) and drive AI/ML-driven protein design to advance in vivo genetic medicines. You'll oversee a team spanning NGS and computational biology, build predictive models, and integrate multi-omics data to guide platform discovery and translation.

Requirements

• PhD in Computational Biology, Bioinformatics, Protein Engineering, or related field with 10+ years industry experience
• Proven research in protein modeling or computational biology
• Hands-on AI/ML experience with protein language models, structure prediction, and generative design
• Expert-level Python or R with data science libraries
• Experience with cloud computing and workflow management systems

Responsibilities

• Lead and develop a team of NGS and computational biology scientists
• Design and optimize LSRs integrating sequence-function, structure-function, and protein-DNA interaction data
• Analyze NGS data to quantify on-target efficiency and genome-wide specificity
• Build and benchmark predictive and generative AI/ML models for protein variant and library design
• Oversee scalable computational pipelines for multi-omics data analysis