Description:
InCommon is hiring on behalf of a fast-growing SF/Bangalore based Voice AI Startup. About the Role This is not a typical data role. You won't be building dashboards. You won't be maintaining pipelines no one touches. You will take messy, noisy, real-world data — and turn it into something models can learn from. Think of this as running a gold mine - you take dust and convert it to gold. We work on speech, language, and real-time systems across 50+ languages. The difference between a good model and a great one is almost always data quality + data systems. That's where you come in. What You'll Work On Data Pipelines (Real-time + Batch) Build high-throughput pipelines for audio, text, and multimodal data Streaming + offline processing at scale Data Quality & Curation Cleaning, filtering, deduplication, normalization (numbers, emails, code-mix, etc.) Designing heuristics + ML-based data filtering systems Multilingual Data Systems Handling 50+ languages, accents, and code-mixed inputs Language-aware normalization and segmentation Training Data Engine Build pipelines that continuously generate better training data from production Active learning loops, data selection, sampling strategies Evaluation & Benchmarking Pipelines Create scalable eval datasets across languages and domains Automate quality tracking for ASR, TTS, and conversational systems Data Infra for Research Work closely with research team to unblock experiments fast Build systems that reduce iteration time from weeks → hours What This Role Is NOT Not a dashboard/reporting role Not a "move data from A to B" role Not a maintenance-heavy legacy pipeline role What We're Looking For Strong fundamentals in data structures, systems, and pipelines Experience with large-scale data processing (audio/text preferred) Co