Discover, create, and share music with the world. Use the latest technology to create AI music in seconds. Backed by a16z.
About the role
We are looking for a full-stack scientist to pioneer quantitative research efforts at Udio.
What they're looking for
- Deep quantitative expertise: Ph.D. in statistics, mathematics, physics, or another quantitative discipline, or 5+ years’ industry experience as a quantitative analyst / data scientist
- Autonomy & ownership: You thrive in greenfield research domains, undefined product categories and small, flat teams. Driven by curiosity and good taste, you ask good questions in addition to finding good answers
- Engineering chops: You’re adept in translating your ideas into clear, production-ready code and collaborating in an active research codebase
- Excellence in scientific communication: You relate technical information with rigor and crystal clarity to researchers, engineers, product managers and business partners alike
More about this role
Udio builds extraordinary AI experiences to empower musical artists and super fans. Pairing best-in-class AI models with groundbreaking partnerships across the music industry, Udio's mission is to champion musicians and expand how fans engage with their favorite music and artists. Udio is backed by leading lights from tech and music, including a16z, Redpoint, Hanwha, will.i.am, Steve Stoute, Kevin Wall, and many others. For more information, please visit udio.com .
We are looking for a full-stack scientist to pioneer quantitative research efforts at Udio.
You will build at the intersection of research, engineering and product, bridging disciplines by drawing on huge, one-of-a-kind proprietary datasets of music, metadata and user interactions/feedback. Working closely with the modeling team, product leadership and the music evaluation manager, you will apply your research toward pushing the frontier of music generation, setting a course through a bleeding-edge product category and unlocking new revenues for artists and experiences for fans.
You’ll dive deep under the hood of our music generation systems, applying computational & human resources to understand model capabilities and...
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