Machine Learning Engineer - Computational Drug Discovery

About this role

Join a computational drug discovery team as an ML engineer to develop and deploy machine learning models that accelerate pharmaceutical research. You'll work on novel algorithms and data pipelines to predict molecular properties and drug efficacy, collaborating with chemists and biologists in a mission-driven biotech environment.

What you'll do

• Design and train machine learning models for molecular property prediction and drug candidate screening
• Build data pipelines and infrastructure to process large-scale chemical and biological datasets
• Collaborate with medicinal chemists to translate domain expertise into ML features and validation strategies
• Evaluate model performance and implement validation frameworks for drug discovery applications
• Optimize computational workflows for handling high-dimensional molecular data
• Contribute to documentation and reproducibility of ML methods and results

What they're looking for

• Machine learning (supervised/unsupervised learning, model evaluation)
• Python programming and scientific libraries (NumPy, Pandas, scikit-learn)
• Data engineering and pipeline development
• Molecular biology or chemistry fundamentals
• Deep learning frameworks (TensorFlow, PyTorch) - preferred
• Cloud computing or distributed computing experience
• Version control (Git) and collaborative development
• Statistical analysis and experimental design

Benefits

• Work on high-impact computational drug discovery research
• Collaborate with experienced chemists and biologists
• Access to cutting-edge ML and computational infrastructure
• Mission-driven biotech environment

Likely interview questions

• Tell us about a machine learning project where you worked with scientific or domain-specific data. What challenges did you encounter?
• Have you worked with molecular or chemical data? How did you handle domain-specific feature engineering?