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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 SummaryYou 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 documentationQualifications
- 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 speechNice 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
