Research Data Engineer at Smallest

RESEARCH DATA ENGINEER (INDIA) — SMALLEST.AI [Upgrade to PRO to see link] ABOUT THE ROLE This is not a typical data engineering 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) - Comfortable with messy, unstructured, real-world data - Strong coding skills (Python required; systems experience is a plus) - Understanding of ML/data pipelines (training, eval, data curation) BONUS (NOT MANDATORY) - Experience with speech/audio data (ASR/TTS) - Familiarity with multilingual datasets - Experience with streaming systems (Kafka, etc.) - Exposure to data-centric AI / data quality frameworks HOW WE WORK - Speed over perfection - Production over papers - Systems that scale, not scripts that barely work - Tight loop between data → model → eval → improvement WHO THIS IS FOR - You enjoy working with raw, chaotic data - You care about data quality more than tooling hype - You like building systems that directly impact model performance - You get excited by turning unusable data into competitive advantage WHY JOIN US We’re building real-time, multilingual voice AI systems. At this level, models are only as good as the data behind them. If you want to work on the layer that actually moves the needle - this is it.