Applied ML Engineer Scientist

You will adapt foundational machine-learning models to drug-discovery problems for external partners. You will curate datasets, tune model architectures and objectives, evaluate applied models, deliver solutions, and feed practical learnings and failure modes back into core model development.

Responsibilities

• Apply and adapt machine-learning models to drug-discovery problems
• Fine-tune, condition, and deploy models for scientific use cases
• Translate partner needs into modeling strategies
• Curate and adapt datasets
• Select and tune model architectures and objectives
• Own problem formulation, experimentation, evaluation, and delivery
• Identify practical gaps and failure modes and improve foundational models

Requirements

• Experience applying machine learning to real-world problems in biology, chemistry, or drug discovery
• Familiarity with computational biology and chemistry datasets, tools, data formats, and workflows
• Experience with PyTorch
• Experience with the scientific Python ecosystem including NumPy, SciPy, and Pandas
• Experience contributing to and maintaining deep-learning codebases
• Knowledge of engineering quality, reproducibility, and testing

Benefits

• Substantial equity ownership