ML Research Scientist

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:

• 2-3+ years in speech.
• Pre-trained a speech or audio model from scratch that reached production.
• 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.