Job Description

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. Competitive compensation and equity.