Designing RNA molecules using deep generative models - Inceptive

At Inceptive, you will drive development forward that could help billions of people. To accomplish this, you will be part of a collaborative, antedisciplinary team building our biological software.

There are more RNA sequences coding for a single target protein than atoms in the universe. Each of these RNA sequences differs in ways that may radically affect how it performs when used in medicine. Faced with this search space, we believe that machine learning based approaches are ideally placed to tackle the RNA design problem that we work on at Inceptive.

You will be responsible for developing and training large-scale machine learning models to generate RNAs with controllable characteristics. You will have access to both public and in-house data sets as well as the opportunity to develop new wet lab methods to generate data working together with colleagues from many different scientific backgrounds including RNA biology.

Additionally, you will establish relevant evaluation benchmarks to guide decision-making and track progress. The designs you create will undergo rigorous testing in our lab, ensuring real-world validation of their potential impact.

Your Mission, should you choose to accept it

• Develop, implement, and train large-scale foundation models on vast biological data sets with applications to various downstream biological properties and controllable molecule design
• 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 build innovative and scalable experiments to solve complex problems that span biology and computation

Qualifications

• Experience building and training large-scale foundation models and/or fine-tuning such models for non-trivial downstream tasks — experience with biological data is a plus
• A solid foundation in both machine learning theory and practice and experience building machine learning systems end-to-end, including data generation and lifecycle management, is preferable
• Problem-solver who is equally excited by finding creative solutions to complex problems, as you are by executing on them
• Ability to communicate technical and scientific concepts to team members from a broad variety of backgrounds
• Ability to understand the big picture, while driving projects on the ground from beginning to end
• Deep knowledge in your traditional area of expertise with a strong eagerness to connect to and apply your skills in other areas
• Availability to accommodate working with team members across different time zones, with one or two meetings per week starting at 8am PT or finishing at 7pm CET
• Readiness to travel several times a year for company retreats and business events
• We value the benefits of in-person collaboration and prefer candidates who can easily and regularly connect at one of our office locations

Compensation

$135K – $240K + Bonus + Equity