Pioneers quantitative research for music generation AI using proprietary datasets, designs evaluation frameworks for models, drives data roadmaps, and builds production infrastructure at the intersection of research, engineering, and product. Requires PhD or 5+ years quant experience with strong coding skills.
250k – 350k
Remote5+ YOEData Science
About the role
What You’ll Do
Design & own evaluation/optimization frameworks for frontier music models
Dive deep under the hood of our music generation systems, applying computational & human resources to understand model capabilities and identify areas for growth
Build optimization loops and apply your findings to our pretraining, post-training and inference systems as applicable
Drive product & research roadmap
Own our data roadmap end-to-end, formulating research questions, exploring/linking/expanding data sources and conducting experiments at your discretion
Work spans data mining, machine learning, causal inference, survey design and more
Build stable infrastructure
Manifest work in robust integrations with our research & product tech stacks, potentially in performance-critical paths
Build large-scale standalone data processing systems
Champion scientific rigor
Cultivate a culture of scientific rigor across the company and deepen common understanding of models, users and data
Proactively identify opportunities, define metrics, share results, and build a rigorous foundation
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: Thrive in greenfield research domains, undefined product categories and small, flat teams
Engineering chops: Adept in translating your ideas into clear, production-ready code and collaborating in an active research codebase
Excellence in scientific communication: 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
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
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