Build data generation, post-training, and evaluation methods to make Cohere's agentic LLMs safer and more secure

Cohere's Safety for Agents team is hiring a senior MTS to work on data generation, post-training algorithms, and evaluation methods for LLMs that can access external tools and take actions in the world. The role blends ML research, experimental design, and hands-on engineering with human-annotator data pipelines, requiring publication-level research credibility (a paper at a top venue like NeurIPS/ICML/ACL) plus strong software engineering. Expect deep technical questions on distributed LLM training, dataset quality/bias analysis, and designing evaluations for agent safety and robustness.

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What this interview tests

Post-training algorithms and evaluation methods for agentic LLM safety
Designing and running data collection tasks with human annotators
Dataset quality, bias, and suitability analysis for training data
Hands-on distributed LLM training experience
Statistical rigor in evaluating scientific/ML experiments
Reasoning about agent-specific failure modes vs. static-generation safety

Common question themes

Describe an experiment you designed to evaluate data collection or model performance, and how you validated it statistically

How would you design an annotation task and catch bias or low-quality labels in the resulting dataset

Walk through your hands-on experience training an LLM on distributed infrastructure and a bottleneck you solved

Tell me about owning an ambiguous safety/research problem from question to shipped evaluation

How do agent-specific safety failure modes differ from standard LLM safety issues, and how would you evaluate for them

Discuss a publication or research contribution and how it applies to industrial safety work

How candidates describe it

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

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