Join Udio as a full-stack scientist to lead quantitative research efforts at the intersection of music and AI.
Posted by employer 7 months ago
First seen on Joblaze 1 week ago
Last verified on the company career page 8 hours ago
Skills & Technologies
AI in the day-to-day
You’ll build optimization loops and apply findings to our pretraining, post-training and inference systems.
Requirements
Not disclosed in this posting: work arrangement, visa sponsorship.
Benefits
Joblaze summary
In this role, the Member of Technical Staff will lead quantitative research initiatives at Udio, focusing on the evaluation and optimization of music generation models. The position requires a strong background in statistics or data science, along with proficiency in coding and data processing. Ideal candidates are those with a Ph.D. or significant industry experience, who thrive in innovative environments and possess a passion for music. Udio's small, interdisciplinary team emphasizes scientific rigor and aims to redefine how fans engage with music.
Joblaze insights
Quick facts
From the original posting
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.
Design & own evaluation/optimization frameworks for frontier music models
You’ll dive deep under the hood of our music generation systems, applying computational & human resources to understand model capabilities and identify areas for growth. You’ll build optimization loops and apply your findings to our pretraining, post-training and inference systems as applicable.
Drive product & research roadmap
You’ll own our data roadmap end-to-end, formulating research questions, exploring/linking/expanding data sources and conducting experiments at your discretion. Your work will span data mining, machine learning, causal inference, survey design and more, and your results will be critical for decision-making in product development, research investment and overall business direction.
Build stable infrastructure
Your work will reach far beyond the jupyter kernel, manifesting in robust integrations with our research & product tech stacks, potentially in performance-critical paths. You’ll also build large-scale standalone data processing systems, allocating resources as needed to manage the data ecosystem.
Champion scientific rigor
As our first quantitative researcher, you’ll cultivate a culture of scientific rigor across the company and deepen common understanding of models, users and data. You’ll proactively identify opportunities, define metrics, share results, and build a rigorous foundation upon which to understand our highly subjective domain.
Udio’s success hinges on hiring great people and creating an environment where we can be happy, feel challenged, and do our best work.
Udio provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability
This role is eligible for a compensation package of base salary, equity, and benefits. The salary range for this role is $250k - $350k. Actual salary may vary based on level, work experience, performance, and other factors evaluated during the hiring process.
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