ML Engineer - Speech

• Fine-tune and adapt open-source speech models on our proprietary call audio
  
• Build training and evaluation pipelines for multilingual and code-mixed speech
  
• Own model quality against metrics that matter operationally — entity and numeric accuracy, latency to first response — not just aggregate error rates
  
• Design and run data curation at scale: pseudo-labelling, speech enhancement, quality filtering on messy real-world audio
  
• Work with our linguist on text normalisation and pronunciation handling
  
• Evaluate candidate architectures, make the call with evidence, and ship the result to production with the platform team
  

Requirements

• 3–4 years in ML, with at least 18 months on speech or audio specifically
  
• Strong Python and PyTorch; comfortable reading a paper and implementing it
  
• Hands-on experience fine-tuning at least one production speech model
  
• Solid grasp of speech fundamentals — mel-spectrograms, acoustic models and vocoders, encoder-decoder vs transducer architectures, evaluation methodology, sampling rates and what they cost you
  
• Understanding of how modern speech systems are actually built: self-supervised encoders, neural audio codecs, LM-based generation, flow matching
  
• Experience with genuinely messy audio, not only clean benchmark datasets
  

• NeMo, ESPnet, SpeechBrain, or Coqui
  
• Telephony-band or contact-centre audio
  
• Multilingual or code-switched speech work
  
• LoRA/PEFT, distributed training
  
• Open-source contributions or publications in speech