Applied Research Scientist, AI Research

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. Some recent work from the team: - Audio editing by latent inpainting : regenerating a masked span of speech  - Video Regenerate : regenerating a speaker's lower face to match new or translated audio - Jumpcut Smoothing : generating a bridge across a cut so the join plays like a continuous take - Anchored Tree Sampling : tree-based imputation that bounds drift in long video generation - PoDAR : disentangling power from semantics in audio latents to make them easier to model More at descript. com/research . What you'll do - Multimodal understanding: build vision-language systems that let Descript's agentic editing features reason over the visual and audio content of a project. - Evaluation: design the benchmarks and evals that make editorial quality measurable, and that balance quality against cost and latency. - Data: build the datasets your work depends on, including synthetic data generation where real examples don't exist at scale. - Training: train specialized models from scratch or fine-tune existing foundation models, whichever gets the capability we need.

- Shipping: take models from prototype to production with the agent and engineering teams. - Direction-setting: identify the next research direction that should become a Descript feature, not just a paper. More senior candidates should expect to own this directly; more junior candidates will grow into it. - Publishing: take your work to academic venues if you'd like. We support it, but it isn't a requirement of the role. What you bring Required - 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. - A track record of generating new ideas in machine learning. You produce more ideas than you can implement, and once an experiment setup is established, you can run and evaluate many of them quickly rather than being bottlenecked on infrastructure. - 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. At least one of the following must be true: - Lead or first author of an accepted publication in a top venue: CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, or similar. - Played a key role in shipping a production feature with deep learning as a core component.