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AI Stem Separation Quality Benchmark Engineer

Victoria Nakamura

Full-time · Job · Los_Angeles

About the role

Source separation models claim impressive SDR scores on standard benchmarks, but those benchmarks don't capture what matters in production: does the separated vocal track have audible artifacts? Does the drum isolation bleed kick drum into the snare channel? Do the separated stems sound natural when played back individually? Our benchmark fills this gap with perceptual quality metrics that correlate with professional audio engineer evaluations. You'll design the benchmark framework: selecting test material that covers the diversity of real-world audio (different genres, recording quality levels, instrumentation density), defining perceptual quality metrics beyond SDR, building the automated evaluation pipeline, and running listening tests that calibrate automated metrics against human perception. The audio quality team is four engineers. You'll own the benchmark methodology, working between the separation model developers who need reliable quality signals and the audio engineers who can hear quality differences that metrics miss.

Responsibilities

  • Design comprehensive benchmark for stem separation quality evaluation
  • Define perceptual metrics that correlate with professional audio engineer judgments
  • Select test material covering diverse genres, quality levels, and instrumentation
  • Build automated evaluation pipelines for reproducible benchmarking
  • Run listening tests calibrating automated metrics against human perception
  • Publish benchmark results used by the separation model development team

Requirements

  • 3+ years in audio ML or signal processing with evaluation experience
  • Understanding of audio quality metrics beyond standard SDR/SIR/SAR
  • Strong Python and PyTorch skills for metric implementation
  • Experience with perceptual evaluation methodology and listening tests
  • Critical listening skills for assessing audio quality

Benefits

  • Fully remote with flexible hours
  • Health and dental insurance
  • $3,000 annual equipment and software budget
  • Access to professional monitoring systems
  • Define quality standards for the audio AI industry

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Categories

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch statistics docker aws

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch statistics docker aws

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