Cohere
Senior
Member of Technical Staff on Cohere's Multilingual team, pushing frontier LLM performance across languages
Cohere is hiring a Member of Technical Staff for its Multilingual modeling team to design and lead scalable solutions that improve multilingual LLM performance and publish research at top-tier venues. This interview tests deep NLP/ML research fundamentals, large-scale data pipeline experience, and the ability to work independently while mentoring others.
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What this interview tests
- Multilingual LLM performance: data, training, and evaluation tradeoffs
- Large-scale data processing and ML pipeline design
- Python and research software engineering best practices
- Research communication: publishing and bridging technical/research audiences
- Independent, self-directed research execution
- Mentoring and shaping best practices for multilingual AI/NLP
Common question themes
Diagnose and improve a multilingual model's weak performance on specific languages
Design a scalable data/training pipeline for multilingual coverage
Tokenization and evaluation challenges across diverse scripts/languages
Walk through a past research project from idea to publication
How you'd mentor a junior researcher on a multilingual NLP problem
Tradeoffs in cross-lingual transfer vs. per-language specialization
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