Cohere Interview Questions — Page 1

Cohere·Machine Learning Engineer·Hiring Manager Screen

Apr 2026

Hiring manager screen at Cohere for an MLE role. Two distinct halves: the first is a pretty standard career background conversation, and the second is a surprisingly deep dive into something specific from your resume. The paper/project portion caught me more off guard than I expected.

Walk me through your career trajectory and what's drawing you to this role and team.
Pick a paper or project from your resume and explain the motivation behind it, your methodology, what you personally contributed, what the results were, and where the work fell short.

“Pretty routine stuff.”

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Cohere·Machine Learning Engineer·Onsite - Coding / Algorithms

Apr 2026

Cohere's ML Engineer live coding round was unlike anything I'd done before. They handed me a working Python text game built on a mess of if/else logic and told me to refactor it using an LLM coding tool of my choice, then prove the behavior hadn't changed. The meta-layer of being evaluated on how I used AI, not just whether the code worked, made it a genuinely interesting format.

You're given a terminal-based text game in Python built with heuristic if/else rules. Refactor the implementation while keeping all externally observable behavior identical, and use AI coding tools to do it.
Walk through your prompting strategy. How did you decide what to ask the LLM to do, and in what order?
How did you verify that the new implementation was equivalent to the original? What gave you confidence and where did you have doubts?
Where did you trust the LLM's output and where did you override or manually fix it? What was your decision process?
How would you harden this kind of LLM-assisted refactoring for a production codebase?

“The actual coding part was less stressful than I expected because the scaffolding was already there.”

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Cohere·Software Engineer·Hiring Manager Screen

Apr 2026

45-minute live session with the hiring manager at Cohere for a data engineer role. The whole thing was a walkthrough of actual work, which felt more like a code review than a traditional interview.

You're currently filling missing values with 0. Why not just drop those records entirely? And why not leave them as NULL instead of replacing them?

“This tripped me up a bit because it sounds like a cleanup question but it's really asking you to justify a design decision under pressure.”

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Cohere·Machine Learning Engineer·Technical Phone Screen

May 2026

Cohere ML Engineer interview that was basically one long open-ended design question about making LLMs better at multi-step reasoning. No coding, no leetcode, just a deep technical conversation that covered a lot of ground fast. Felt more like a research discussion than a traditional interview.

Given a task that requires multi-step reasoning, such as math word problems, agentic tool use, or code generation with planning, how would you improve an LLM's performance on it? Walk through your full approach across data, training, inference, evaluation, and cost trade-offs.

“This question is deceptively wide.”

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Cohere·Software Engineer·Take-home Assignment

May 2026

Take-home for a data engineering role at Cohere. Seven days to complete but they suggested about 90 minutes, which felt weirdly optimistic given the scope. The scenario was realistic enough that I actually enjoyed it, though I probably overthought the idempotency part.

Design the table schema (DDL or typed column list) to store daily conversation log data loaded from JSONL files in object storage. Each record includes a conversation ID, user ID, start timestamp, model version, and a nested messages array with per-message fields like role, content, token counts, and latency.
Write pseudo code for parsing and validating each JSONL record, including handling malformed JSON lines, unexpected data types, impossible numeric values, and deduplication logic.
How would you schedule the daily ingestion job and ensure it is idempotent so re-runs don't produce duplicate or inconsistent data?

“The nested messages array is the interesting bit here.”

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Cohere·Machine Learning Engineer·Technical Phone Screen

May 2026

This was a stretch/bonus portion of a Cohere ML engineer interview, only unlocked after finishing the greedy and top-k decoding sections. The nucleus sampling implementation was the hardest part and felt like a real test of whether you actually understand the math behind sampling strategies.

Implement nucleus (top-p) sampling: given input tokens and a probability threshold p, at each decoding step sort tokens by probability, take the smallest set whose cumulative probability exceeds p, renormalize over that set, sample, and repeat autoregressively until EOS.

“This only showed up after clearing greedy and top-k, so by the time I got here my brain was already pretty fried.”

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Cohere·Software Engineer·Technical Phone Screen

May 2026

Interviewed for a data engineering role at Cohere, got one pretty meaty technical question about deduplication at scale. Not a lot of context given upfront, so I had to figure out what they were really asking as I went.

How would you implement deduplication across an entire table, not just within a single day's ingestion batch?

“This tripped me up more than it should have.”

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Cohere·Software Engineer·Technical Phone Screen

Jun 2026

Interviewed for a Data Engineer role at Cohere. Just one technical question from what I can piece together, focused on Spark performance issues. Short and to the point.

A Spark job is running and one worker node is experiencing data skew. How do you diagnose and fix it?

“This is bread and butter Spark stuff but I still fumbled the ordering a bit.”

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Cohere·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Second round coding interview for an MLE role at Cohere. They asked me to implement top-K sampling using NumPy and I completely fell apart because I'd only practiced PyTorch. The interviewer was patient and dropped hints, but I left feeling like I'd wasted everyone's time.

Implement top-K sampling from a probability distribution using NumPy.

“I froze.”

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Cohere·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Round 3 at Cohere for an ML Engineer role, which was a research paper presentation. Beyond just summarizing the paper, they pushed hard on its limitations and how the field had moved since publication, plus the usual SFT and RLHF territory.

What are the limitations of the experimental setup in the paper you presented?
How has the field evolved since this paper was published, and what does that mean for its conclusions?
Walk me through how supervised fine-tuning works and where it fits in the alignment pipeline.
Explain the core mechanics of RLHF and the tradeoffs involved in reward model training.

“This is where I felt the most pressure.”

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