ML Research Engineer, Speech

What you will do

• Pre-train STT models from scratch (Conformer/Zipformer encoders, CTC/RNN-T/TDT decoders, self-supervised pre-training like wav2vec 2.0/HuBERT/BEST-RQ), then fine-tune them for Indian languages, Hinglish and BFSI vocabulary
• Train TTS models from scratch (flow-matching, diffusion, neural codec LMs, vocoders) and extend them to new voices, languages and speaking styles
• Build speech-to-speech components from scratch: audio tokenizers and codecs, speech encoders connected to LLMs, streaming decoders
• Turn papers and new ideas into working PyTorch code: write the model, tokenizer and training loop yourself, and train from random initialisation
• Build data pipelines: cleaning, segmentation, forced alignment, pseudo-labelling, augmentation (noise, codecs, 8 kHz telephony simulation)
• Maintain evaluation suites: WER/CER, entity error rate, code-switch accuracy, MOS and speaker similarity, latency (time to first token / byte)
• Optimise models for serving: quantisation, streaming chunking, ONNX/TensorRT export, batching on GPUs
• Analyse production errors, turn them into test sets, and close the loop with the platform team
• Write clear experiment reports and share findings in weekly research reviews

What you bring

Nice to have

• Speak or understand Hindi or another Indian language
• Publications, Kaggle/benchmark results, or open-source contributions in speech
• Exposure to real-time audio (WebRTC, SIP, streaming inference) or telephony audio
• Familiarity with LLM fine-tuning (LoRA, SFT) or audio-language models

What success looks like in 6 months