About the job
At Inductive Bio, we are revolutionizing the drug discovery process by leveraging artificial intelligence to develop predictive models. Scientists traditionally design molecules based on predictions of their potency, safety, and absorption, yet the validation of these predictions through laboratory experiments can be immensely time-consuming and costly. Our innovative approach enables us to create in silico models that accurately forecast molecular behavior, allowing scientists to make informed decisions more rapidly, ultimately accelerating the delivery of safer, high-quality medicines to patients. Our extensive proprietary dataset supports our ongoing application of these advanced methodologies in numerous active drug discovery initiatives. With backing from prominent investors straddling both biotechnology and technology sectors, and guidance from esteemed drug discovery experts, we are on the cusp of a significant transformation in the industry.
We are looking for passionate Machine Learning Scientists to join our dynamic and collaborative team. In this role, you will have the chance to innovate on algorithms, collaborate with leading drug discovery scientists, and apply your work directly to pioneering drug programs at some of the world’s most innovative biotech companies. As one of the early machine learning scientists at Inductive Bio, you will have the opportunity to make a substantial impact while evolving alongside our rapidly growing company.
Key Responsibilities:
Develop and refine machine learning models to predict molecular properties from chemical structures.
Create innovative algorithms for generating novel molecule ideas.
Build intelligent agents that synthesize complex data from drug programs to guide molecular optimization.
Engage deeply with our unique proprietary datasets to iterate on modeling concepts and enhance model performance.
Collaborate closely with chemists and software engineers to seamlessly integrate models into our software platform used by drug discovery scientists worldwide.
Construct and optimize scalable infrastructure for model training, deployment, and monitoring.
Interact directly with scientific users to gather feedback and improve the product.
Contribute significantly to product strategy and the overall direction of the company.

