
Research Scientist
1 week ago
About Josh Talks (AI Division)
Josh Talks' AI arm builds and licenses large-scale Indian speech datasets in over 12 languages. Our datasets are used by global AI labs and enterprises to train, evaluate, and localize models for Indian users. We're now expanding into evaluations, multimodal benchmarking, and fine-tuning foundational models grounded in India's linguistic and cultural diversity.
Role Summary
You will serve as the research backbone of our conversational speech initiatives - ensuring that the data we create is research-grade, aligned to ASR/LLM needs, and fine-tuned for impact. This includes working with our internal data teams, product leads, and partner labs to define what to build next and validate what we deliver.
Key Responsibilities
Design and validate data collection protocols for Indian conversational speech (phone calls, debates, dialogues)
Define evaluation benchmarks (e.g., WER, CER, emotion/filler metrics, lip sync alignment)
Perform regular spot-checks of ASR output and transcripts for quality assurance
Train lightweight ASR/embedding models on in-house data for internal experiments
Collaborate on multilingual and multi-modal expansions (e.g., video+audio evals)
Stay updated on speech model trends (e.g., Whisper, SeamlessM4T, Gemini 1.5 Pro) and suggest roadmap pivots
Write whitepapers, benchmark results, and internal documentation
Qualifications
Master's or PhD in CS, Computational Linguistics, AI/ML, or a related field
Experience working with ASR models (Whisper, Wav2Vec, Conformer, etc.)
Proficient in Python, PyTorch/TensorFlow, HuggingFace or ESPnet/NeMo
Comfortable with audio processing tools (FFmpeg, Librosa, etc.)
Familiarity with Indian language scripts and challenges in low-resource speech
Nice to Have
Experience with fine-tuning or benchmarking Whisper, MMS, or SeamlessM4T
Experience working with speech datasets (Common Voice, MLS, OpenSLR, internal corpora)
Multilingual fluency in any Indian language beyond English/Hindi
Publication or contribution to an open-source speech project
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