Ready to own the data pipeline powering the voice of the next generation of AI characters?You'll be joining a well‐funded startup building AI character technology, where speech is a core part of the product experience. Think super natural conversations, handling interruptions, personality shifts and more!
You'll own the datasets that power their speech systems — from raw, messy audio through to clean, versioned training corpora that directly drive TTS and ASR model performance.
Your focus
Own the full data lifecycle — defining specs, auditing and curating large‐scale audio and text corpora
Build automated quality metrics and dashboards across SNR, VAD, WER, speaker verification and safety, validated against listening tests
Train and deploy lightweight classifiers for noise detection, diarisation, language ID, and content moderation
What you'll bring
Deep experience working with speech and audio data at scale — 1M+ hours
Strong ML engineering skills in Python and PyTorch, including training and fine‐tuning models like Whisper or Wav2Vec
Practical knowledge of audio processing — torchaudio, librosa, spectrograms, DSP basics
A solid understanding of audio quality metrics — MOS, WER, PESQ/STOI, SNR, speaker verification
Nice to have
Experience with Spark/Beam, Airflow, SQL or similar data engineering tools
Open‐source contributions or publications in speech or audio ML
Background in denoising and enhancement, and how it affects downstream model quality
Remote, with a preference for European or overlapping timezones.
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