Cohere
Senior
Build data tooling for Cohere's Safety team
Senior Research Engineer role on Cohere's Modeling Safety and Trust team, owning the vision for data pipeline tooling that enables synthesis, analysis, and management of real and synthetic data used in model training and evaluation. Remote-friendly (UK/Europe/ET timezones), the interview centers on software engineering rigor combined with statistical evaluation of experiments and ML data infrastructure.
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What this interview tests
- Data pipeline and tooling architecture for ML training/eval
- Statistical rigor in evaluating experiments
- Python, PyTorch, and big-data tooling (BigQuery/SQL)
- Technical ownership and opinionated architecture decisions
- Gap analysis of data sources and benchmark coverage
- Collaboration with research scientists on experimentation needs
Common question themes
Describe a data pipeline or tooling repository you owned from design to deployment
Tell me about a time you made an opinionated architecture call and defended it to a team
How have you evaluated a scientific experiment involving data collection or model performance?
Walk me through a specific pipeline you built using Python, PyTorch, and BigQuery/SQL
Describe how you identified gaps in data sources or benchmark coverage
How do you balance building for real vs. synthetic data across training and evaluation?
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