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

- Robust 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

📌 ML Engineer - Speech (Noida)
🏢 Rezo.AI
📍 Noida