ML Research Engineer, Speech

About the role

You will build speech models from the ground up: implement architectures from scratch, pre-train them on large audio corpora, then adapt and ship them. You'll own well-scoped experiments from data to deployed checkpoint, with guidance from senior researchers.

What you'll do

• 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)

What you need

• 1–3 yrs of experience
• Machine learning
• Research
• Python
• PyTorch
• MLOps
• LLMs
• B.Tech or B.E. or M.Tech or M.Sc or PhD

Good to have

• ETL