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AI Audio Restoration and Noise Removal Engineer

Victoria Nakamura

Freelance · Job · Amsterdam

About the role

Archival recordings are degrading faster than our current tools can restore them. We believe decades of recorded music and spoken history shouldn't disappear because nobody built a good enough restoration model in time. You'll extend our existing stem separation and mastering pipeline with dedicated restoration models: removing tape hiss, clicks, and hum from degraded source recordings while preserving the original performance's character rather than flattening it into something generic. Why this is worth doing: some of the archives we work with are the only surviving copies of a recording. A model that over-cleans or introduces artifacts doesn't just lose fidelity, it can erase details in something irreplaceable.

Responsibilities

  • Build noise and artifact removal models for degraded archival audio
  • Extend existing stem separation pipeline with restoration-specific processing
  • Preserve original performance characteristics rather than over-cleaning recordings
  • Evaluate restoration quality against archivist and engineer feedback
  • Document restoration parameters for reproducibility on future archives

Requirements

  • Experience with audio signal processing and restoration techniques
  • Comfortable with PyTorch or TensorFlow for audio model development
  • Understanding of common audio degradation patterns in archival recordings
  • Appreciation for preserving original recording character over generic cleanup

Benefits

  • Remote freelance engagement, project-based
  • Flexible scheduling around project milestones
  • Portfolio credit on restored archival releases
  • Potential for repeat engagements on future archive projects

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Job Type

Freelance

Level

Mid-Level

Language

English

Salary Range

Negotiable / Competitive

AI Expertise

AI & Machine Learning Engineers

Skills

python pytorch tensorflow numpy docker

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