Cohere
Mid
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.
走进这场面试
免费 · 一场按这个岗位校准的真语音模拟
这场面试考什么
- 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
常见提问方向
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
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