Machine learning integrated with chemistry/biology at scale lies at the core of insitro’s approach to rethinking drug discovery and development. To accomplish that, we are putting together a team of life scientists with expertise in the design, use, and application of microfluidic devices and microfluidic electrophoresis. Responsibilities in this role will include acquiring, designing, and/or building devices needed for performing high-resolution analyses and separations of very complex sample mixtures, and / or analytes that are exceptionally hard to separate. You will also acquire accurate and precise data to build data sets with many different proteins and biomolecules, and working as part of a cross-functional team to automate collection of biological and chemical data for drug discovery. You will work in concert with numerous cross-functional teams, comprising machine learning engineers, life scientists, automation engineers and data scientists. You will demonstrate world-class research skills and experience in developing and using cutting-edge molecular biology methods. You will hone your interpersonal skills, and build a strong collaborative work ethic, as well as the ability to adapt to changing needs, and the capacity to multitask to meet key goals. Exceptional attention to detail and communication skills are essential to success in this position.
Specifically, you will:
You will be joining the founding team of a biotech startup that has long-term stability due to significant funding, but yet is very much in formation. Much can change in this early and exciting phase, providing many opportunities for significant impact. You will work closely with an exceptionally talented team, learn a broad range of skills, and help shape insitro’s culture, strategic direction, and outcomes. Join us, and help make a difference to patients!
Nice to Have
insitro is a data-driven drug discovery and development company using machine learning and data generation at scale to transform the way that drugs are discovered and delivered to patients. insitro’s approach focuses on the development of predictive machine learning models to be brought to bear on key bottlenecks in pharmaceutical R&D. The company has established enabling collaborations with Gilead in NASH and Bristol Myers Squibb in ALS. insitro is located in South San Francisco, CA and has been supported by top tech, biotech, and crossover investors since formation in 2018. For more information on insitro, please visit the company’s website at www.insitro.com.
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