
Job Description
Exonic Biosciences · Dogpatch, San Francisco
$120K–$180K a year + equity
Exonic is building AI systems for programmable biology.
We train multimodal foundation models on large-scale biological datasets to design synthetic non-coding DNA. We experimentally validate our AI-designed systems in mammalian cells, with current applications in cell-type targeting and viral vector biomanufacturing.
Our core focus is synthetic regulatory DNA. We have already generated strong in vitro cell-type targeting results and are expanding our internal experimental platform in San Francisco.
We are looking for an ambitious experimental scientist to take ownership of Exonic’s wet-lab work and help push the platform into new directions.
You would work directly with the founder as one of the first people in the company.
What you’ll work on
- Own and execute experimental validation of AI-designed regulatory DNA
- Design and run cell-based assays for synthetic enhancer and expression systems
- Help advance programs in cell-type targeting and AAV manufacturing
- Help shape Exonic’s experimental roadmap as the company grows
Relevant experience
- Molecular biology, synthetic biology, regulatory genomics, or gene therapy
- Mammalian cell culture and cell-based assays
- Cloning, plasmid design, transfection, qPCR/ddPCR, sequencing, flow, luciferase, or related workflows
- AAV biology/manufacturing experience is a major plus
We care most about ownership, execution speed, and comfort with ambiguity.
This is an early-stage role with autonomy, and the opportunity to build the biological engine of an AI-native synthetic DNA company.
Location: San Francisco / MBC BioLabs
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Job Details
- Category
- Research
- Employment Type
- Full Time
- Location
- San Francisco, CA
- Posted
- Compensation
- $120,000 - $180,000 per year
About Exonic
What if you could train a biological foundation model on... the entire internet? Exonic is pioneering a new generation of biological foundation models, focused on heterogeneous, unstructured, and noisy datasets. Our first application is the design of safer gene therapies. In 2025, we used AI to set a new state of the art in liver cancer targeted gene therapy, validated in vitro in our lab in San Francisco. So far in 2026, we have trained a new model with unprecedented zero-shot generalization on genomic regulatory expression in hold-out biosample datasets. More to be shared soon.
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