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

How candidates describe it

Real Senior Member of Technical Staff interview stories — retold from candidates' public write-ups, with sources.

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