Responsibilities :

- Train speech-to-text (STT) and text-to-speech (TTS) models from scratch.

- Adapt models for Indian languages, Hinglish, and banking/insurance vocabulary.

- Build parts of speech-to-speech systems (tokenizers, codecs, streaming decoders).

- Build data pipelines : cleaning, labeling, and augmentation.

- Evaluate models (WER, entity accuracy, latency) and optimize them for deployment.

- Analyze production errors and turn them into test sets.

- Senior level (3+ years) : Own a research area (STT, TTS, or speech-to-speech), pre-train large models on 100k+ hours of audio, define benchmarks, and mentor 1-3 engineers.

Requirements :

- 1-6 years in ML, with at least one real speech or audio project.

- B. Tech/M. Tech/MS in CS, EE, or a related field.

- Strong Python and PyTorch; able to build a model and training loop yourself.

- Speech basics : spectrograms, mel features, CTC, seq2seq.

- Experience with one of NeMo, ESPnet, Hugging Face, fairseq, or K2/icefall.

- Trained at least one model from scratch (multi-GPU, mixed precision, W&B/MLflow).

- Careful evaluation habits.

- For 3+ years, also :

1. 2-3+ years in speech.

2. Pre-trained a speech or audio model from scratch that reached production.

3. Distributed training experience (FSDP/DeepSpeed, multi-node).

Nice to have :

- Hindi or another Indian language.

- Publications or open-source work.

- Real-time or telephony audio experience.