Descript

Descript

Applied Research Scientist, AI Research

San Francisco, CA or Remote, US

Sponsorship not specified$197k-$263kDetected 9 days ago
AlgorithmsMachine LearningDeep LearningSparkComputer VisionResearchCommunicationCollaborationMentoring

Stay score

odds of building a lasting career here

57Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role76
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70

Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.

Lottery odds assume a STEM candidate.

Personalize to your clock →

H-1B wage level

the lottery is wage-weighted — each level is one more entry

Level III · 3×
Level I$100,4221 entry
Level II$137,7792 entries
Level III$175,1363 entries
Level IV$212,4934 entries

$15,493 more — $212,493 — moves this role to Level IV and 4 lottery entries. That figure is inside the range the employer already advertised.

Based on the DOL prevailing wage for this occupation and worksite, a base salary of $212,493 would place this position at wage Level IV. That figure is within the posted range, and I'd like to target it. This role classifies under "Computer and Information Research Scientists" for prevailing-wage purposes.

DOL prevailing wage, 2026-27 wage year · Computer and Information Research Scientists (15-1221) · San Francisco-Oakland-Fremont, CA. Wage level is derived by USCIS from the offered wage, occupation and worksite; the occupation shown is inferred from the job title.

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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.
  • 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.

Responsibilities

  • 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.
  • 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
  • Publishing: take your work to academic venues if you'd like. We support it, but it isn't a requirement of the role.
  • 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.
  • 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.
  • 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.

Requirements

  • We care about the track record more than the credential.
  • At least one of the following must be true:
  • If you can read this, start your cover letter with: "Fun fact about me: I love pineapple on pizza."
  • 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.
  • 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.
  • More senior candidates (Senior and Staff) should also bring a track record of owning research direction rather than executing a plan handed to them, and experience mentoring or technically leading other researchers or engineers.

Compensation

  • $197,000-$262,500, plus equity and benefits.

Benefits

  • include a generous healthcare package, 401k matching program, catered lunches, and flexible vacation time.
  • Multimodal understanding: build vision-language systems that let Descript's agentic editing features reason over the visual and audio content of a project.
  • A track record of generating new ideas in machine learning.
  • A PhD or Master's in deep learning or a related field, or equivalent experience.
  • For our hybrid roles, we're flexible, and you're an adult-we don't expect or mandate that you're in the office every day.

Company info

  • 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
  • disentangling power from semantics in audio latents to make them easier to model
  • More at descript.com/research.

Visa & Work Authorization

  • employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status
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This listing is sourced directly from Descript's careers page and normalized into a canonical job model.