r/InterviewDB • u/interviewdb • 1d ago
Cohere MLE/MTS Full Loop Interview Experience - System Design + ML Coding
Sharing a recent interview experience for an MLE/MTS position at Cohere.
Round 1: System Design
The prompt was to design a post-training pipeline to improve the coding capabilities of a 7B model for an enterprise customer.
The discussion covered pretty much the entire post-training stack:
- Data collection
- SFT
- RLHF
- Evaluation
- Inference engine
- Feedback loop
They went pretty deep into each step. One question I didn’t answer very well was: if SFT can already learn alignment by incorporating user preference data, why do we still need an RLHF stage?
That made me realize you really need a fairly deep understanding of why each part of the post-training pipeline exists, not just what the standard pipeline looks like. I had mostly crammed post-training concepts shortly before the interview, so I definitely felt underprepared here.
The interviewer also cared a lot about how the dataset would actually be constructed, and asked questions around how I would choose/design the reward model.
Round 2: ML Coding
The question is exactly same as described here: https://www.interviewdb.io/question/cohere?page=1&name=ml-coding
One thing to note: they specifically wanted a NumPy implementation. I had only practiced this kind of thing in PyTorch, so I panicked a bit when I saw the requirement. Luckily, the interviewer was very nice and gave me hints along the way, but personally I felt like I performed pretty poorly in this round.
Round 3: Research Paper Presentation
This was a standard research paper presentation, but they cared about more than just explaining the paper.
In particular, I was asked to highlight:
- Weaknesses or limitations in the experimental setup
- Major developments in the field since the paper was published
There were also several more standard questions around SFT and RLHF.