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?

How candidates describe it

Real Senior Research Engineer interview stories — retold from candidates' public write-ups, with sources.

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