ML Research Intern (SUMMER) – MS/PhD (Computational Biology)

Genesis Therapeutics is building a world-class software team to solve problems in drug discovery through machine learning, biophysical simulation, and computational chemistry. We are looking for engineers excited to help develop new medicines and play a critical role in building out our software platform.

Where ML Meets Biomedicine—Innovate This Summer

• Join our Computational Biology Principal Scientist to push the boundaries of drug discovery by leveraging advanced graph neural networks to integrate multi-modal genomics data with protein-protein interaction networks—transforming AI into real-world biomedical breakthroughs.
• Our successful candidate will embark on a dynamic 12-week summer internship, tackling impactful projects at the intersection of AI and drug discovery. You’ll implement cutting-edge graph-based machine learning models, develop integration pipelines for multi-modal omics datasets, and build robust validation frameworks for druggability predictions. You’ll contribute to documentation and reproducible analysis workflows, ensuring your research leaves a lasting impact. The internship culminates in a final presentation and report, giving you the opportunity to showcase your work and shape the future of AI-driven biomedicine.

You will

• Develop and implement graph neural network architectures to capture protein-protein interaction networks
• Integrate and analyze GWAS, functional genomics data with existing druggability features
• Validate predictions using Open Targets data
• Document methodology and results
• Present findings to the research team

You are

• Currently enrolled in a graduate program in Computational Biology, Bioinformatics, Computer Science, or related field
• A strong Python programmer
• Experienced with machine learning frameworks (PyTorch, TensorFlow, or similar)
• Knowledgable of biological networks and genomics data analysis
• Strong analytical and problem-solving skills

You will stand out if you have

• Experience with graph neural networks or graph embedding techniques
• Familiarity with drug discovery concepts and terminology
• Previous work with biological network analysis
• Experience with large-scale genomics data processing
• Knowledge of drug target identification methods

To apply, please submit a cover letter detailing your interest in the role and relevant experience, along with your academic transcript and contact information for two academic references. We’re looking for passionate researchers eager to push the boundaries of AI in drug discovery—if that sounds like you, we encourage you to apply and join us in shaping the future of computational biology!