Udio — Member of Technical Staff - Quantitative Research

job posting·professional role·active

Official active posting for Member of Technical Staff - Quantitative Research at Udio, located in New York City (Remote possible for exceptional candidates).

Verified facts

Official site	https://job-boards.greenhouse.io/udio/jobs/5081608008 ↗
Geography	United States
job board posting id	5081608008
team	unknown
location	New York City (Remote possible for exceptional candidates)
seniority	staff_or_principal
department	Technology
exact title	Member of Technical Staff - Quantitative Research
role family	machine_learning_and_research
closing date	unknown
compensation	raw: $250k - $350k · period: year · maximum: 350000 · minimum: 250000 · currency: USD · disclosed: true · parse state: parsed
posting date	2026-01-22
employer name	Udio
posting status	active
workplace type	remote-possible
employment type	unknown
required skills	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.
selection basis	official_current_board_freshness_or_domain_critical_role
employer subtype	generative_music_company
preferred skills	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.
responsibilities	We are looking for a full-stack scientist to pioneer quantitative research efforts at Udio.; 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.
work authorization	unknown
education requirements	unknown