ML Engineer - Speech

Responsibilities:

• 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

Must have skills:

• 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

Nice to have:

• 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