AI Scientist - AI Molecular Design
New
J
JobgetherBiotechnology
Based in the United StatesFull-TimeMiddle
SalaryExpected compensation ranging from $134,000 to $161,000
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Job Details
- Required Skills
- PythonPyTorchTensorflowscikit-learnLangChain
Requirements
- Hold a Master’s degree or PhD in Computational Biology, Computer Science, Physics, Applied Mathematics, or another relevant quantitative discipline, with practical experience in a related technical field.
- Have hands-on coding and analytical experience with Python.
- Have experience with Scikit-learn, PyTorch, TensorFlow, and RDKit.
- Be able to develop, implement, test, and apply new or adapted algorithms through original coding.
- Be able to translate scientific and technical requirements into scalable, reliable, maintainable production software.
- Understand protein structure prediction and computational design algorithms.
- Have practical experience modifying, extending, or improving tools such as Chai-1, Boltz-2, BindCraft, BoltzGen, Mosaic, or Proteina Complexa.
- Have experience with wet-lab validation methodologies for AI-generated molecular designs, including DMTA cycles and using experimental data to train and optimize AI models.
- Be familiar with experimental approaches to antibody or VHH discovery, particularly hit identification and optimization.
- Preferred: life sciences, diagnostics, or biotechnology industry experience; agentic AI frameworks such as LangChain or LangGraph; LLM APIs through Hugging Face, OpenAI, or Anthropic; or NGS data and sequence analysis.
Responsibilities
- Design, develop, test, and deploy machine learning and artificial intelligence models for molecular design.
- Develop AI solutions for protein and binder design, mRNA optimization, antibody optimization, and molecular engineering.
- Implement, adapt, and improve computational algorithms for molecular design challenges.
- Apply computational approaches and molecular modeling tools to protein structure prediction, protein engineering, and binder design.
- Coordinate assay scheduling, execution, data collection, quality control, and reporting with experimental teams.
- Ensure experimental datasets are accurate, consistent, and standardized for model training and evaluation.
- Collaborate on Design-Make-Test-Analyze (DMTA) cycles, using experimental results to validate designs and improve models.
- Analyze experimental feedback and model outcomes to refine algorithms and molecular design workflows.
- Document computational workflows, methodologies, experimental findings, and model performance.
- Collaborate with scientific, computational, and business teams to deliver AI-driven solutions.
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