Computational Biologist
Job description
Bio is a decentralized science protocol that helps launch and grow AI-driven biotech research. It enables scientists to raise funds, create value from their work, and distribute that value directly to their communities. Since 2023, Bio has directed over $50m to global researchers, offering an alternative to traditional pharma funding. Backed by investors like Binance Labs, Northpond Ventures, and Animoca Brands, Bio accelerates real-world therapeutics across longevity, brain health, fertility, psychedelic science, and more.
THE ROLE
We are looking for a computational biologist to serve as the bridge between our AI systems and physical reality. We need someone who can programmatically QA the high-volume outputs of our AI before they reach the bench, catching errors that are invisible to humans but obvious in the data.
WHAT YOU'LL DO
- Validate AI outputs using computational methods, including simulations, structural analysis, and database cross-referencing
- Translate AI predictions into executable protocols for cloud labs (Emerald, Strateos) or technical specs for CROs
- Build and maintain validation pipelines that catch errors before they waste lab resources
- Manage the hand-off between digital predictions and physical experiments
- Transform JSON outputs from our agents into experimental protocols and validation workflows
WHAT WE'RE LOOKING FOR
- PhD in Computational Biology, Bioinformatics, or a related field preferred, or equivalent exceptional experience
- Strong proficiency in Python and/or R for data analysis and pipeline development
- Experience with computational validation methods, including molecular simulations, structural analysis, and sequence analysis
- Ability to critically evaluate biological predictions and identify potential issues
- Comfort working at the interface of software and biology
NICE TO HAVE
- Experience with cloud lab platforms, including Emerald, Strateos, or similar
- Background working with CROs or translating computational work to wet-lab execution
- Familiarity with AI/ML systems and their failure modes
- Experience red-teaming or adversarially testing scientific predictions
WHY BIO
- Work at the cutting edge of AI-driven biology
- Fully remote, work from anywhere
- Small team where your expertise directly shapes our science
- Competitive compensation with token/equity component
About the role
This role focuses on ensuring that AI-generated biological predictions are accurate and actionable. You will use computational methods to review and verify high-volume model outputs. The goal is to prevent errors from advancing into laboratory work. This position sits at the intersection of AI predictions and experimental biology.
Key facts
What you'll do
- Review AI model outputs using simulations, structural analysis, and database checks
- Convert AI predictions into protocols for cloud labs or specifications for contract research organizations
- Create validation workflows that detect problems before experiments use resources
- Oversee the transition from digital predictions to physical laboratory procedures
- Change JSON results from AI agents into experimental steps and validation plans
- Support the use of cloud platforms such as Emerald and Strateos
- Work with CRO teams to align computational results with practical execution
- Build systems that stop flawed predictions from reaching the bench
Requirements
- PhD in Computational Biology, Bioinformatics, or a related field or equivalent exceptional experience
- Strong Python and or R skills for data analysis and pipeline development
- Experience with computational validation methods such as molecular simulations, structural analysis, and sequence analysis
- Capacity to assess biological predictions and identify potential issues
- Ability to work at the interface of software and biology
Nice to have
- Experience with cloud lab platforms such as Emerald, Strateos, or similar systems
- Background working with CROs or translating computational work to wet lab execution
- Familiarity with AI and machine learning systems and their failure modes
- Experience red teaming or adversarially testing scientific predictions
Skills & tools
- Python
- R
- Cloud lab platforms (Emerald, Strateos)
- Database systems
- Molecular simulations
- Structural analysis
- Sequence analysis