insitro's mission is to use AI and machine learning to develop medicines for patients with diseases that have little to no effective treatment. As a field, drug discovery and development has faced a decades-long decline in productivity characterized by high failure rates in the clinic, primarily due to a lack of understanding the underlying biology of disease. This is despite billions spent to build models of biology, whether in the lab or animal models, to predict clinical success. Ultimately, we need better predictive models.
Requirements
- Ph.D. in computer science, mathematics, biology, bioinformatics, or a related discipline, or equivalent practical experience
- 12+ years of real-world experience in applying AI/ML methods to cellular data
- Demonstrated in-depth knowledge in the foundations and practice of modern machine learning, including deep learning
- Experience with both imaging and omics modalities
- Experience in the use of cellular data in the context of drug discovery efforts a nice to have
- Peer reviewed publications in high-quality conferences or journals
Responsibilities
- Work collaboratively with our Drug Discovery leaders on the strategy for deployment of advanced ML to empower target discovery and biological insights, driving the design of experiments that generate data that enables the use of advanced ML methods
- Lead and grow a team of outstanding machine learning scientists
- Onboard and develop ML and bioinformatic methods for interpreting complex multi-modal perturbation data. Relevant topics include representation learning, contrastive approaches to isolating salient features, disentanglement approaches for interpretability and statistical power, multi-modal embedding, uncertainty modeling, and more.
- Engineer robust, reusable platform components in partnership with the software engineering team
- Work with our platform biology and automation teams to advance our platform capabilities, including both: (i) the development of new wet lab capabilities and (ii) the advancement of new ML methods, including by generation of data explicitly designed for training ML models
- Collaborate with the corporate development and strategy teams to drive discussions with potential external partners and maximize the value created by our platform technologies
Other
- Lead and grow a team of outstanding machine learning scientists
- Lead yearly and quarterly planning, set impactful goals, and align with cross-functional stakeholders
- Experience in people management and leadership, including building and developing team members based on the strategic needs of the company
- Demonstrated ability to collaborate effectively with life science colleagues to form research and therapeutic pipeline execution roadmaps.
- Demonstrated ability to lead a team of scientists and engineers to plan, execute and deliver a full machine learning solution for challenging, real-world problems: sourcing and qualifying training data; designing and implementing machine learning models; testing & benchmarking; shipping stable, robust, high-performance code.