ML Engineer, Drug Discovery & De Novo Design

SupportFinity™; Boston (MA); On-site; USD 140,000 - 210,000; Full time

Job summary

Superluminal Medicines in Boston seeks a Machine Learning Developer/Engineer to join the discovery team. You will develop and deploy state-of-the-art ML models to generate quantitative predictions that drive drug discovery, and serve as a scientific partner to chemists and biologists.

You will design and deploy active learning loops that use experimental assay results to iteratively improve models for de novo design, ADMET predictions, and affinity forecasts.

Qualifications

• Ph.D. preferred in Computer Science, Machine Learning, Engineering or a related field, or BS/MS + seasoned experience.
• Proven experience with protein-ligand co-folding algorithms and integrating insights into broader ML discovery pipelines.
• Advanced proficiency in Python and deep learning libraries (PyTorch, TensorFlow) and production-quality code and data pipelines.
• Exceptional ability to communicate the 'why' behind a design to a diverse scientific audience.

Responsibilities

• 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 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 de novo design, ADMET predictions, and affinity predictions.

Skills

Python

Deep learning

Production code

Communication

Education

PhD in Computer Science / ML or related

BS/MS + seasoned experience

Tools

PyTorch

TensorFlow

RDKit

scikit-learn

numpy

pandas

PyG/DGL

DeepChem

polars

Docker

Kubernetes

CI/CD

data governance

Job description

Superluminal Medicines in Boston seeks a Machine Learning Developer/Engineer to join the discovery team. You will develop and deploy state-of-the-art ML models to generate quantitative predictions that drive drug discovery, and serve as a scientific partner to chemists and biologists.

You will design and deploy active learning loops that use experimental assay results to iteratively improve models for de novo design, ADMET predictions, and affinity forecasts.