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Pooled screening of RNA libraries to advance ML design (Palo Alto, CA)
You will be responsible for the development and deployment of novel high-throughput assays to measure various properties of large RNA libraries. You will use a data-driven approach to optimize assays and apply chemical and biological insights to maximize the utility of information collected. You will apply this data to efficiently improve design of therapeutic RNA molecules in close collaboration with biologists, ML and data engineers, and translational experts.
Your Mission, should you choose to accept it
Your Mission, should you choose to accept it
- Embody our vision of an antedisciplinary environment and embrace learning about areas outside of your traditional area of expertise
- Collaborate with team members from diverse backgrounds to create experiments that creatively leverage biology and computation
- Develop and execute pooled assays to profile RNA function in vitro, in cells, and in vivo
- Generate, analyze, and communicate data that maximally impacts AI-based molecular design
- PhD, plus postdoc or 2+ years biotech experience
- Problem-solver who combines deep knowledge of experimental methods with quantitative intuition to generate biological data at and beyond the state-of-the-art
- Solid foundation in theory and techniques of molecular and cellular biology
- History of building highly multiplexed assays to generate rich datasets from mammalian cells, such as pooled genetic screens, single-cell ‘omics, and complex genetic reporters
- Availability to work with team members across US and Europe, with meetings starting at 8am PT
- Readiness to travel several times a year for company retreats and business events
- We value in-person collaboration and expect candidates to work at our lab location
- Experience with synthetic biology, particularly in mammalian systems
- Knowledge of RNA biology and biochemistry, and biologics drug development
- Deep, applied understanding of next-generation sequencing platforms (Illumina and Oxford Nanopore), including library generation and data analysis
- Experience generating and analyzing large ‘omics datasets, via sequencing and/or mass spectrometry
- Ability to visualize data and perform statistical analysis, preferably in Python
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