Machine Learning Engineer

About Superluminal Medicines:

Superluminal Medicines is a generative biology and chemistry company revolutionizing the speed and accuracy of how small molecule medicines are created. The Company’s platform aims to create candidate-ready compounds with unprecedented speed using a combination of deep biology, computational and medicinal chemistry, machine learning, and proprietary big data infrastructure. We are expanding the team of talented scientists who seek to build the future of small molecule drug discovery with creativity and innovation.

About the Role:

We are seeking a high-impact Machine Learning Developer/Engineer to join our integrated discovery team. In this role, you will be the algorithmic engine of our programs, developing and deploying state-of-the-art ML models to generate the quantitative predictions necessary to drive drug discovery. Beyond technical mastery, you will serve as a core scientific partner to medicinal chemists, computational chemists, and biologists, building models that move programs efficiently toward Go/No-Go decision points and candidate nomination.

Key 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 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

Required 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 (e.g., Boltz, AlphaFold, OpenFold, etc) and the ability to integrate these structural insights into broader ML discovery pipelines.

• Advanced proficiency in Python and deep learning libraries (e.g., PyTorch, TensorFlow) is required. You must be capable of building and maintaining production-quality code and data pipelines.

• Exceptional ability to communicate the "why" behind a design to a diverse scientific audience.

Preferred Qualifications:

• Expert-level knowledge of deep learning frameworks, specifically for affinity prediction, ADMET modeling, and the application of LLMs in a biological or chemical context

• Expertise fine-tuning existing models with internally generated structural biology and biology data

• Experience deploying ML/AI algorithms for use by a cross-functional scientific audience

• 1-4+ years of experience in a biotech or pharma setting

Skills & Competencies:

• A demonstrated track record of innovation in the ML/AI space, including developing and validating new architectures or novel applications of existing models to solve complex drug discovery problems including tools for hit identification (virtual screening, HTS)

• Expert level use of protein-ligand co-folding algorithms to small molecule drug discovery ML/AI tools (AlphaFold, Boltz, OpenFold)

• Experience writing production-level code for ML tasks:

• Knowledge of key scientific packages (RDKit, scikit-learn, numpy, pandas, pytorch, deepchem, polars, PyG/DGL):

• Write robust, testable, and version-controlled code that adheres to CI/CD and data governance best practices.

• Value clarity, documentation, and structured thinking, especially when working with complex data

• Knowledge of containerization technologies (Docker, Kubernetes) and cloud deployment at scale

Equal Opportunity Statement:

Superluminal Medicines is an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race; color; creed; religion; national origin; age; ancestry; nationality; marital, domestic partnership or civil union status; sex, gender, gender identity or expression; affectional or sexual orientation; disability; veteran or military status or liability for military status.