Build custom, customer-facing LLM solutions on Cohere's foundation-model stack

This role sits between research and customer delivery: taking ambiguous enterprise problems, framing them as ML problems, and building custom models using Cohere's CPT and post-training stack (including RLVR), then feeding learnings back into Cohere's frontier models. The interview should test both ML fundamentals/LLM depth and the judgment to operate with startup-level ownership on ambiguous, high-stakes customer problems.

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What this interview tests

Framing ambiguous business problems as ML problems
LLM customization: CPT, post-training, RLVR
Evaluation methodology design
Retrieval-augmentation and agent integration
High-ownership execution in ambiguous, customer-facing settings

Common question themes

Walk me through turning an ambiguous enterprise problem into a scoped ML problem with success criteria

How do you decide between fine-tuning, CPT, retrieval augmentation, or agent orchestration for a customer use case

Describe an evaluation framework you built to prove a customized model actually improved on the baseline

Tell me about a technique or insight from one customer project that fed back into a broader model capability

Describe a time you had to define your own scope and success criteria on an ambiguous project

How candidates describe it

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

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