ML Engineer - Speech

Gohyred; Uttar Pradesh; On-site; INR 1,500,000 - 2,200,000; Full time

Job summary

Gohyred in Noida, Uttar Pradesh, India, is seeking an ML engineer specializing in speech to fine-tune models, build end-to-end pipelines, and improve real-world audio performance. The role demands hands-on work with multilingual data and a focus on latency-sensitive deployment.

Candidates should have 3–4 years in ML with substantial speech experience, strong Python/PyTorch skills, and a track record of deploying speech models. A collaborative environment with cross-functional teams is offered.

Qualifications

• 3–4 years in ML, with at least 18 months on speech or audio
• Strong Python and PyTorch; comfortableReading a paper and implementing it
• Hands-on experience fine-tuning at least one production speech model
• Solid grasp of speech fundamentals — mel-spectrograms, acoustic models and vocoders, encoder–decoder vs transducer architectures

Responsibilities

• Fine-tune and adapt open-source speech models on proprietary call audio
• Build training and evaluation pipelines for multilingual and code-mixed speech
• Own model quality against operational metrics like entity and numeric accuracy and latency
• Design and run data curation at scale: pseudo-labelling and quality filtering on real-world audio
• Collaborate with linguists on text normalization and pronunciation handling
• Evaluate candidate architectures and ship results to production with the platform team

Skills

Python

PyTorch

Speech model fine-tuning

Audio processing

Reading papers

Tools

NeMo

ESPnet

SpeechBrain

Coqui

Job description

Job Information

• Date Opened 28/09/2026
• Job Type Full time
• Industry Technology
• Work Experience 4\\-5 years
• City Noida
• Province Uttar Pradesh
• Country India
• Postal Code 201301

Job Description

Responsibilities

• Fine\\-tune and adapt open\\-source speech models on our proprietary call audio
• Build training and evaluation pipelines for multilingual and code\\-mixed speech
• Own model quality against metrics that matter operationally — entity and numeric accuracy, latency to first response — not just aggregate error rates
• Design and run data curation at scale: pseudo\\-labelling, speech enhancement, quality filtering on messy real\\-world audio
• Work with our linguist on text normalisation and pronunciation handling
• Evaluate candidate architectures, make the call with evidence, and ship the result to production with the platform team

Requirements

Must have skills

• 3–4 years in ML, with at least 18 months on speech or audio specifically
• Strong Python and PyTorch; comfortable reading a paper and implementing it
• Hands\\-on experience fine\\-tuning at least one production speech model
• Solid grasp of speech fundamentals — mel\\-spectrograms, acoustic models and vocoders, encoder\\-decoder vs transducer architectures, evaluation methodology, sampling rates and what they cost you
• Understanding of how modern speech systems are actually built: self\\-supervised encoders, neural audio codecs, LM\\-based generation, flow matching
• Experience with genuinely messy audio, not only clean benchmark datasets

Nice to have

• NeMo, ESPnet, SpeechBrain, or Coqui
• Telephony\\-band or contact\\-centre audio
• Multilingual or code\\-switched speech work
• LoRA/PEFT, distributed training
• Open\\-source contributions or publications in speech