Descript is the AI video and audio editor that makes editing as easy as editing text. Record, transcribe, edit, and publish in one tool. Try it free. Backed by a16z and Redpoint.
About the role
This role is focused on multimodal understanding: training models to perceive edited media the way a human video editor does. Underlord, our AI editing agent, reasons about a project largely through a textual representation of it. Giving it direct perception of the media it's working on is what will let it judge its own output and reason about the creative choices in an edit, not just the structure of a project.
What they're looking for
- Proven ability to design and implement deep learning algorithms, demonstrated by publications, open-source work, or models you've shipped
- Strong programming skills and deep fluency in PyTorch
- Strong experimental judgment. You test ideas fast, and you're honest with yourself and the team about which ones don't pan out
- Clear written and verbal communication, including when a direction isn't working, so the team doesn't waste time following a lead that's already dead
- A PhD or Master's in deep learning or a related field, or equivalent experience. We care about the track record more than the credential
More about this role
Descript's Research team builds the models behind the product's most distinctive features: Video Regenerate and lipsync, video translation, zero-shot voice and roomtone cloning, and Studio Sound. We don't build general-purpose generative models. We pick specific problems in the editing workflow and build specialized models for them. This isn't research for its own sake. Everything we build is meant to ship, and most of it has, going from prototype to a production feature used by millions of creators within months.
This role is focused on multimodal understanding: training models to perceive edited media the way a human video editor does. Underlord, our AI editing agent, reasons about a project largely through a textual representation of it. Giving it direct perception of the media it's working on is what will let it judge its own output and reason about the creative choices in an edit, not just the structure of a project. It's also an open research problem, since there's no settled way to represent or evaluate editorial craft, whether a cut lands or whether the pacing works. We have a unique dataset to work with.
- Audio editing by latent inpainting : regenerating a masked span of...
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