GeneEase Computational Bioengineer Gene Therapy Vector Design

About Us

GeneEase discovers novel gene therapies for subclinical conditions overlooked by traditional medicine - genetic disorders affecting millions (alcohol intolerance, lactose intolerance, mild autoimmunity) where existing treatments are inadequate or nonexistent.

We use computational biology to identify promising therapeutic targets, design gene therapy approaches (AAV, base editing, other modalities), validate mechanisms in silico, and partner with wet labs for experimental confirmation. We file patents and license IP to gene therapy companies for clinical development.

Role Overview

Lead computational gene therapy development: design therapeutics that can be manufactured, delivered, and clinically translated - not just theoretically promising targets.

You'll evaluate targets, design vectors with practical constraints in mind (manufacturability, delivery, immunogenicity), validate in silico, and coordinate academic partnerships for experimental validation.

80% computational work, 20% wet lab coordination. You design experiments—partners execute them. Critical: You must understand what makes gene therapies succeed or fail in practice, not just in silico.

Key Responsibilities

Therapeutic Design with Translational Focus (40%)

• Evaluate genetic targets (genetic clarity, deliverability, market size)
• Design gene therapy approaches considering practical constraints:
  • Manufacturability: AAV titer requirements, production scalability, purification
  • Deliverability: Tissue accessibility, dose requirements, injection volumes, immunogenicity
  • Regulatory feasibility: IND pathway, safety assessment, clinical endpoint definition
• Select modality (AAV, base editing, other), vector/delivery system, regulatory elements
• Optimize constructs (codon usage, cargo size, tissue specificity)

In Silico Validation (40%)

• Protein modeling: Structure prediction (AlphaFold), molecular dynamics (GROMACS), enzyme kinetics
• Safety assessment: Biodistribution (PBPK), immunogenicity, off-target prediction
• Translational risk assessment: Therapeutic window, dose-response, durability predictions
• Generate validation reports with confidence scores and translational risk flags

Lab Partnerships (15%)

• Identify labs with relevant capabilities (animal models, assays, AAV production)
• Design experimental protocols considering practical feasibility (realistic timelines, achievable titers, appropriate controls)
• Analyze results vs. predictions, troubleshoot failures, iterate designs

Communication & IP (5%)

• Draft manuscripts, patent applications, pharma partner dossiers emphasizing clinical translatability

Requirements

Education: PhD in Computational Biology, Bioinformatics, Bioengineering, Systems Biology, or related fields (or MS with 3-5 years experience)

Wet Lab Experience: 1-3 years hands-on experience (cloning, cell culture, transfection, assays). Gene therapy experience strongly preferred (AAV/lentivirus production, transduction, functional validation, titer quantification).

Gene Therapy Technical: Vector design (AAV serotypes, promoter selection, cargo optimization, CRISPR guide RNAs), tools (Benchling, SnapGene)

Gene Therapy Development Mindset:

• Understand what makes vectors succeed vs. fail in practice (not just in silico)
• Consider manufacturability, deliverability, and immunogenicity upfront
• Think in terms of "Can this be produced at scale? Delivered to target tissue? Tolerated by patients?"

Computational: Protein modeling (AlphaFold, Rosetta), molecular dynamics (GROMACS/AMBER), pathway analysis (STRING, Reactome), PBPK modeling

Programming: Python (BioPython, pandas, PyTorch), R (Bioconductor), SQL, Git, Linux/HPC

Nice-to-Haves

• Gene therapy industry experience: Worked at gene therapy biotech in vector development, process development, or CMC roles
• IND-enabling study experience: Familiarity with GLP tox, biodistribution studies, dose escalation strategy
• AAV manufacturing knowledge: Production optimization, titer improvement, purification troubleshooting
• Academic/CRO partnership management: Protocol transfer, feasibility assessment, troubleshooting
• Regulatory awareness: IND/CTA filings, FDA feedback meetings, clinical endpoint selection

Essential Traits

• Autonomous problem-solver with mechanistic thinking
• Bridges computational predictions with biological reality
• Builds tools, not just uses them
• Communicates complex work to non-technical stakeholders

Why Join Us

• Shape the foundation of a product that bridges biology and computation.
• Collaborate with a global, interdisciplinary team.
• Flexible, remote-first engagement.
• Opportunity to see your expertise directly influence product development.