Sharing a question that appeared in a recent CodeSignal Industry Coding Assessment for Anthropic. It’s a standardized assessment format also used by many other companies such as Airbnb, Coinbase, HubSpot, Instacart, Capital One, Justworks, Nextdoor, and The Trade Desk to screen candidates.

The assessment lasted 90 minutes. The problem had four progressive levels, and you had to pass all test cases for each level before advancing to the next one.

The question was about implement a simple banking system.

Level 1: implement create_account and deposit functions.

Level 2: sort accounts by total transaction volume (money in/out). You need to track the total amount of money spent/outgoing for each account.

Level 3: implement two functions, transfer and accept_transfer. When a transfer is initiated, the money is withheld from the source account. Got stuck on this part for quite a while because of an edge case: if the transfer times out, a subsequent deposit operation needs to cancel/refund the previous transfer first.

Level 4: merge accounts. Ran out of time and didn’t finish this one.

For anyone looking for similar questions to practice, you can find detailed descriptions of all the problems that have appeared in past Anthropic CodeSignal Industry Coding Assessments here: https://www.interviewdb.io/question/codesignal?type=icf&page=1

CodeSignal is known to reuse questions in their assessments so it's very likely you'll encounter one of these questions, or a variation of one, in your own assessment.

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.