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AI Audio Engineer

Victoria Nakamura

Full-time · Job · Los_Angeles

About the role

We're building AI tools for podcast producers — automatic transcription, speaker diarization, content summarization, and smart clip extraction. Our platform processes 2,000 hours of audio per day, and the models behind it need to handle accents, cross-talk, background noise, and the unpredictable chaos of unscripted conversation. You'll work on the audio ML pipeline: improving our speech recognition accuracy, building better speaker separation models, and developing the audio feature extraction layer that feeds our downstream NLP systems. The stack is PyTorch for model training, TensorFlow for edge inference, and AWS for everything else. This role sits at the intersection of audio signal processing and deep learning. If you've worked on speech or audio ML and you care about the difference between 92% and 97% word error rate, we want to talk.

Responsibilities

  • Improve speech recognition accuracy across diverse audio conditions
  • Build and refine speaker diarization models for multi-speaker content
  • Design audio feature extraction pipelines for downstream NLP tasks
  • Optimize model inference for processing 2,000+ hours of audio daily
  • Collaborate with the NLP team on transcription-to-summary pipelines

Requirements

  • 3+ years in ML with focus on audio or speech processing
  • Strong PyTorch experience with audio model architectures
  • Understanding of signal processing fundamentals (FFT, spectrograms, MFCCs)
  • Experience with Docker and cloud deployment on AWS
  • Bonus: familiarity with ASR frameworks (Whisper, wav2vec)

Benefits

  • Fully remote — US West Coast hours preferred
  • Competitive salary with equity
  • $2,500 conference and learning budget
  • Free premium podcast tools subscription

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Categories

AI & Machine Learning Engineers

Skills

python pytorch tensorflow docker aws

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Job Type

Full-time

Level

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Language

English

Salary Range

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AI Expertise

AI & Machine Learning Engineers

Skills

python pytorch tensorflow docker aws

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