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

1-3 years in ML, with at least one real project in speech or audio (industry, research lab, or a strong thesis)

B.Tech/M.Tech/MS in CS, EE or a related field; a PhD is not required

Strong Python and PyTorch; able to implement a model architecture and training loop from scratch, not just call APIs or fine-tune checkpoints

Working knowledge of speech basics: spectrograms, MFCC/mel features, CTC, attention-based seq2seq, vocoders

Hands-on with at least one of: NeMo, ESPnet, Hugging Face Transformers/Audio, fairseq, Coqui, k2/icefall

Has trained at least one model from random initialisation (speech, audio or language), including multi-GPU jobs, mixed precision and experiment tracking (W&B, MLflow)

Careful with evaluation: you know why a WER number can mislead, and you check your data

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

Contributed to a pre-trained-from-scratch model that shipped to production with a measured gain on a customer-relevant test set

Owns a piece of the evaluation or data pipeline that others rely on

Runs experiments independently and reports results the team can trust