Research Data Engineer

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.

• 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

• Not a dashboard/reporting role

• Not a “move data from A to B” role

• Not a maintenance-heavy legacy pipeline role

• 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)

• 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
  

• Speed over perfection

• Production over papers

• Systems that scale, not scripts that barely work

• Tight loop between data → model → eval → improvement

• 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

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.