Cohere
Senior
Own the web-data pipeline that feeds Cohere's pretraining corpus
This is a senior data/ML-infra role at Cohere focused on turning raw web crawl data into high-quality pretraining data — extraction, dedup, filtering, and quality scoring at large scale. Interview will probe Python data-pipeline engineering, distributed processing frameworks, and judgment about data quality's effect on model performance.
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What this interview tests
- Large-scale web data extraction & parsing
- Deduplication at scale (algorithms & tradeoffs)
- Data quality scoring / filtering design
- Data pipeline engineering in Python (Spark/Beam/Pandas)
- Analyzing data composition's effect on model performance
Common question themes
Walk through a large-scale data pipeline you owned end to end
How would you design a deduplication system for billions of web documents
How do you decide whether a filtering heuristic actually improves training data quality
Tradeoffs between Spark/Beam/Pandas for different data volumes
How would you study the impact of a data mixture change on downstream model behavior
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