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Member of Technical Staff - Quantitative Research

Key Facts

Remote From: 
Full time
Senior (5-10 years)
English

Other Skills

  • Mathematics
  • Communication
  • Problem Solving
  • Teamwork
  • Curiosity

Roles & Responsibilities

  • Ph.D. in statistics, mathematics, physics, or another quantitative discipline, or 5+ years’ industry experience as a quantitative analyst / data scientist
  • Thrives in greenfield research domains and small, flat teams
  • Adept in translating ideas into clear, production-ready code
  • Excellence in scientific communication

Requirements:

  • Design evaluation/optimization frameworks for frontier music models
  • Drive product research roadmap, formulating research questions and conducting experiments
  • Build stable infrastructure and large-scale standalone data processing systems
  • Champion scientific rigor and cultivate a culture of scientific understanding

Job description

About Uncharted/Udio

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.

About the Role

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.

What You’ll Do

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.

 

What We’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.

 

Nice to Have

  • Obsession with music & the science of sound. Experience in DSP, MIR, music production / composition / performance, and a big record collection all a huge plus.
  • Familiarity with deep learning frameworks, especially JAX.
  • Experience with GCP, Apache Beam/DataFlow, Kubernetes, TensorFlow Data / TFRecord.
  • Experience designing evaluation frameworks specifically for generative model outputs in any modality.

 

Why Join Us

  • Work at the intersection of science & art with a bleeding-edge music ML ecosystem, one-of-a-kind proprietary datasets and a passionate userbase.
  • Deploy substantial research compute resources as needed, including a powerful personal TPU pod.
  • Join a small, ambitious team which highly values autonomy, interdisciplinarity and continual learning.

 

Benefits

  • Highly competitive salary and equity
  • Quarterly productivity budget
  • Flexible time off
  • Productivity package, including ChatGPT Plus, Claude Code, and Copilot
  • Top-notch private health, dental, and vision insurance for you and your dependents
  • 401(k) plan options with 5% employer matching
  • Concierge medical and primary care through One Medical and Rightway
  • Mental health support from Spring Health
  • Personalized life insurance, travel assistance, and many other perks

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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