Founding Scientist (Synthetic Genomics)

at Exonic — Unsupervised Biological AI

San Francisco, CA, USFull-time3+ years$120K - $180K0.50% - 2.50% equityYC-F25

What the role involves

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

What they ask for

Cell biologyMolecular biologyWet labBiology

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.

Full Exonic profile

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