About ChemT Biotechnology
ChemT Biotechnology is an AI-first biotech company transforming biologics and cell therapy manufacturing through computational design of small molecules that modulate cell behavior. Our platform integrates machine learning, chemistry, and biology to design next-generation molecules that improve cell expansion, quality, and scalability.
Role Overview
We are seeking a highly motivated scientist to design and optimize small-molecule compounds using AI-driven and computational approaches. You will work at the core of ChemT’s discovery platform, translating biological objectives into molecular design strategies and advancing compounds from in-silico design to experimental validation.
Key Responsibilities
• Design and optimize small-molecule compounds using:
o Machine learning models
o Molecular simulations
o Structure-based and ligand-based methods
• Collaborate with biologists to:
o Translate cell-state or phenotype objectives into molecular hypotheses
o Interpret experimental results and refine models
• Build and improve computational pipelines for:
o Molecular generation
o Property prediction (potency, toxicity, stability, etc.)
• Analyze multi-modal data (chemical, biological, experimental)
• Contribute to IP generation, patents, and technical documentation
• Support scale-up of ChemT’s AI discovery platform across multiple cell types
Required Qualifications
• PhD or MSc in:
o Computational chemistry
o Chemistry
o Chemical biology
o Bioinformatics
o Machine learning (with strong chemistry exposure)
• Strong foundation in
small-molecule drug design
• Experience with at least one of:
o Molecular docking
o QSAR
o Generative models for molecules
o Molecular dynamics
• Programming experience in
Python
(required)
• Ability to work in a fast-moving startup environment
Preferred Qualifications
• Experience applying machine learning to molecular design
• Familiarity with:
o Deep learning (PyTorch / TensorFlow)
o Graph neural networks (GNNs)
• Exposure to:
o Cell therapy, biologics, or manufacturing processes
o Experimental validation workflows
• Prior startup or early-stage biotech experience
