Description

<div class="content-intro">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.</div>About the Role: We are seeking a high-impact Machine Learning Scientist to join our integrated discovery team. In this role, leading from the bench, you will enable the development, validation and deployment of 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 strategic partner to medicinal chemists, computational chemists, and biologists, building models that move programs efficiently toward Go/No-Go decision points and candidate nomination.&nbsp; This role may be responsible for the management and development of individual team members. <h1>Key Responsibilities:</h1> <ul> <li style="font-size: 12pt; font-family: helvetica, arial, sans-serif;">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</li> <li style="font-size: 12pt; font-family: helvetica, arial, sans-serif;">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</li> <li style="font-size: 12pt; font-family: helvetica, arial, sans-serif;">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</li> <li style="font-size: 12pt; font-family: helvetica, arial, sans-serif;">May be responsible for management and development of internal team members</li> </ul> <h1>Required Qualifications:</h1> <ul> <li style="font-size: 12pt; font-family: helvetica, arial, sans-serif;">Ph.D. in Computational Chemistry, Computer Science, Machine Learning, or a related field</li> <li style="font-size: 12pt; font-family: helvetica, arial, sans-serif;">Demonstrated expertise in statistics, probability theory, data modeling, machine learning algorithms, and the languages used to implement analytics solutions</li> <li style="font-size: 12pt; font-family: helvetica, arial, sans-serif;">4-7+ years of experience in a biotech or pharma setting performing ML support for small molecule drug discovery with clear evidence of impact on drug discovery programs</li> <li style="font-size: 12pt; font-family: helvetica, arial, sans-serif;">Demonstrated success in a cross-functional environment, including biologists, structural biologists, medicinal and computational chemists, with specific examples of computational designs/algorithms/models that directly led to the achievement of program milestones</li> <li style="font-size: 12pt; font-family: helvetica, arial, sans-serif;">Expert proficiency in Python and deep learning libraries (e.g., PyTorch, TensorFlow) is required. You must be able to build and maintain production-quality code and data pipelines</li> </ul> <h1>Preferred Qualifications:</h1> <ul> <li style="font-size: 12pt; font-family: helvetica, arial, sans-serif;">Proven experience with protein-ligand co-folding models (e.g.,Boltz, OpenFold, AlphaFold, etc) and the ability to integrate these structural insights into broader ML discovery pipelines</li> <li style="font-size: 12pt; font-family: helvetica, arial, sans-serif;">Expertise fine-tuning existing models with internally generated structural biology and biology data</li> <li style="font-size: 12pt; font-family: helvetica, arial, sans-serif;">Expert-level knowledge of deep learning frameworks, specifically for affinity prediction, ADMET modeling, and the application of LLMs in a biological or chemical context</li> </ul> <h1>Skills &amp; Competencies:</h1> <ul> <li style="font-size: 12pt; font-family: helvetica, arial, sans-serif;">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</li> <li style="font-size: 12pt; font-family: helvetica, arial, sans-serif;">Demonstrated expertise using small molecule drug discovery ML/AI tools (AlphaFold, Boltz, OpenFold, ChemProp, DeepChem, Reinvent, etc)&nbsp;&nbsp;</li> <li style="font-size: 12pt; font-family: helvetica, arial, sans-serif;">Expert level coding for ML tasks including knowledge of key packages (RDKit, scikit-learn, numpy, pandas, pytorch, DeepChem, polars, PyG/DGL).</li> <li style="font-size: 12pt; font-family: helvetica, arial, sans-serif;">Strong interpersonal and communications skills in the "why" behind a design to a diverse scientific audience</li> <li style="font-size: 12pt; font-family: helvetica, arial, sans-serif;">Experience mentoring and developing teams</li> </ul><div class="content-conclusion">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.</div>

Principal Scientist/Associate Director, Machine Learning

Description

Superluminal Medicines