Senior Scientist, Machine Learning — Computational Drug Discovery  

**Location:** New York City/Hybrid

**Company:** Excelsior Sciences

About Us

Excelsior Sciences is a New York City–based startup at the forefront of technology-driven drug discovery. We combine cutting-edge advances in artificial intelligence, laboratory automation, and our proprietary chemistry platform to redefine how small-molecule leads are identified and optimized.

We are building a world-class team of scientists and engineers passionate about transforming therapeutic discovery. As a Machine Learning Scientist, you will play a key role in developing and scaling our computational platform, working at the exciting intersection of chemistry, biology, and AI. This is an opportunity to make a tangible impact in a fast-paced, collaborative environment where your ideas and expertise will directly shape the future of our technology and our mission.

What You’ll Do

• Design, implement, and refine machine learning models for molecular property prediction, generative chemistry, and active learning.
• Analyze and model large-scale molecular datasets to inform the selection of reagents and the design of libraries.
• Collaborate closely with synthetic chemists and automation engineers to translate computational insights into actionable laboratory strategies.
• Contribute to the development of scalable infrastructure and workflows for model training, evaluation, and deployment.
• Stay abreast of emerging methods and literature, proactively bringing innovative ideas into our discovery platform.

What We’re Looking For

We are seeking a senior-level scientist with deep expertise in machine learning applied to drug discovery and a demonstrated ability to translate computational methods into real-world impact. This role is ideal for someone who combines technical excellence with a collaborative mindset and a passion for advancing therapeutic discovery.

• PhD in Machine Learning, Computational Chemistry, Cheminformatics, Computer Science, or a related discipline.
• At least 3 to 5 years of hands-on experience applying machine learning in a drug discovery or cheminformatics context, with a track record of impactful contributions.
• Proven expertise with advanced methods such as Bayesian optimization, uncertainty quantification, generative models, and/or active learning in molecular design.
• Deep expertise in machine learning libraries and frameworks (e.g., PyTorch, scikit-learn, GPyTorch) and cheminformatics toolkits (e.g., RDKit, Open Babel).
• Strong understanding of molecular representations (SMILES, graphs, fingerprints) and familiarity with pharmaceutical and chemical datasets.
• Proficiency in Python and experience working in a collaborative, cross-disciplinary environment.
• Excellent communication skills, intellectual curiosity, and the ability to thrive in a fast-paced, mission-driven startup.

What We Offer

• Competitive salary- Base Salary Range- $145,000-$195,000 and meaningful equity.
• A collaborative, mission-driven team culture that values creativity, rigor, and excellence.

Excelsior Sciences of New York Is an equal opportunity employer (EEO).  We provide equal employment opportunities (EEO) to all employees and applicants for employment without regard to religion, race, creed, color, sex, sexual orientation, alienage or citizenship status, national origin, age, marital status, pregnancy, disability, veteran or military status, predisposing genetic characteristics or any other characteristic protected by applicable federal, state or local law.

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