Machine Learning Scientist - Chemistry
An early-stage biotech startup applying machine learning and computational chemistry to accelerate small molecule drug discovery. This is a unique opportunity to join at the ground level, contribute to strategic decisions, and work on creative, high-impact science. You'll collaborate across disciplines-chemistry, biology, and data science-while driving innovation in lead optimization and predictive modeling.
Key Responsibilities
- Build and deploy deep learning architectures for generative modeling (e.g., VAEs, flow-based models) to design novel compounds.
- Engineer robust ML pipelines and infrastructure for large-scale chemical and biological datasets.
- Integrate computational chemistry techniques (QSAR, docking, molecular modeling) with advanced ML approaches.
- Collaborate with experimental teams to validate computational insights and iterate on design strategies.
- Contribute to strategic planning for platform development and next-generation agentic AI products.
- Operate independently and adapt to a fast-paced, startup environment.
Experience
- Strong ML engineering experience with a focus on scalable model deployment and optimization.
- Hands-on expertise in deep learning frameworks (e.g., PyTorch, TensorFlow) and generative modeling techniques (VAEs, flow-based models).
- Demonstrated success in predictive modeling for toxicity and ADMET properties.
- Proven ability to work independently and take ownership of complex projects.
Education
- Ph.D. in Computational Chemistry, Chemistry, or a closely related field.
- Strong publication record in top-tier journals demonstrating expertise in computational chemistry and/or ML applications in drug discovery.
Tags & focus areas
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