About this role
RESEARCH DATA ENGINEER (INDIA) — SMALLEST.AI http://Smallest.ai
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.
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.
Tech stack
PythonKafka
About Smallest
Smallest is hiring for the research data engineer role. NewJob aggregates active openings directly from Smallest's applicant tracking system, so this listing is current.
More jobs at Smallest →