Superluminal Medicines Inc.

Machine Learning Engineer

• Implement algorithms for hit identification through virtual screening and other high throughput computational methods as part of a cross-functional team
• Adapt and implement cutting-edge ML architectures for co-folding to augment our extensive internal structural biology expertise and capabilities
• Design and deploy active learning frameworks that utilize experimental assay results to iteratively improve model performance and reduce the number of "Design-Make-Test-Analyze" cycles leveraging state-of-the-art de novo design, ADMET predictions, and affinity predictions

Research Scientist

• Lead the application of Large Language Models (LLMs), co-folding algorithms, and generative chemistry techniques to design novel chemical matter aimed at hitting key program milestones, such as establishing selectivity windows and optimizing drug-like properties
• ML lead on project teams, collaborating intimately with medicinal chemists to refine SAR and with structural biologists to integrate co-folding and structure-based insights into ML workflows
• Data-Driven Decision Making: Synthesize complex ML outputs into clear, actionable design hypotheses that cross-functional scientific stakeholders can use to make high-stakes program decisions
• May be responsible for management and development of internal team members.

Machine Learning Engineer

Principal Scientist – Machine Learning