Voice AI Researcher at Humyn Labs

Bengaluru, Karnataka, India · noida · full time · mid level

About the role

## About Humyn Labs Humyn Labs builds the intelligence layer for physical-world AI — systems that perceive, reason and act in real environments. The work sits at the intersection of egocentric video understanding, embodied AI, robotics perception and voice-driven interaction. Humyn Labs converts human action across sound, sight, movement and touch into high-quality multi-modal data signals for physical AI, operating across 20+ countries in India, Southeast Asia, Latin America and the Middle East — the real-world environments where physical AI deploys, not the labs where it is built. The data is not just collected; it is evaluated, defended and production-ready. ## Role overview Humyn Labs is building structured, high-quality voice datasets for frontier AI companies working on speech-to-text, speech-to-speech and multimodal AI systems. This role rigorously evaluates datasets across evolving speech models, identifies performance gaps across Indic and global languages, and publishes those findings as structured research for the broader AI community. ## What you will work on - Benchmark voice datasets across ASR and speech models — Whisper, Deepgram, Google STT, Azure Speech and emerging open-source models. - Measure performance using WER, CER, MOS, robustness, latency and error-pattern analysis. - Design structured experiments to understand how dataset characteristics affect model accuracy, and compare across multilingual, dialect-heavy, emotional and noisy speech. - Systematically identify where speech models underperform: Indic languages and dialects (Hindi, Tamil, Telugu, Bengali, Kannada), code-switching and transliteration, emotional and conversational speech, low-resource scenarios, and real-world background noise. - Quantify model weaknesses through reproducible analysis, and map each gap to the dataset requirement that would close it — helping define what data models actually need next. - Build a standardised dataset quality scoring rubric with measurable criteria: audio clarity, speaker diversity, annotation accuracy, emotion depth and accent/dialect coverage. - Tag and rank data suppliers against objective quality signals. - Publish benchmarking findings as blog posts and LinkedIn articles readable by technical and non-technical audiences. - Contribute to internal evaluation reports tracking performance shifts as new models ship, and share outside-in insight with AI research teams and clients. ## You must have - 1–3 years in speech AI, audio ML, NLP or applied AI research. - Hands-on experience with ASR/TTS systems and an understanding of model behaviour. - Exposure to running experiments, evaluating models and designing evaluation frameworks. - Strong Python and comfort with ML experimentation workflows. - Genuine interest in linguistic diversity, particularly Indic languages, and how models perform across them. - Strong written communication — able to turn research into clear, publishable content. ## Technical skills - Python (mandatory). - PyTorch or TensorFlow. - Whisper, SpeechBrain, Kaldi or similar toolkits. - Familiarity with WER, CER, MOS and SNR metrics. - Experience with multilingual or low-resource datasets (preferred). ## Ideal mindset - Curious about model failure modes, not just model capabilities. - Analytical and detail-oriented, with a bias for reproducibility. - Comfortable reading research papers and independently testing new APIs. - Keen to share work publicly — blogs, LinkedIn, open datasets.

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Voice AI Researcher at Humyn Labs

About the role

All 56 open roles

Voice AI Researcher at Humyn Labs

About the role

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