This company's interview format is pretty different from everywhere else, so it's worth writing up on its own. Of the four rounds, only one was a traditional coding round in the usual sense — the rest were all "here's a small requirement, write code that could ship to production." They use their own online environment, where you can run tests and also look things up in the docs. So pure template memorization doesn't help much here; how clean your code is and whether your tests actually cover things carries a lot of weight.
The problem names below were all given to me as coded homophones rather than their real names, so I'm describing what they actually asked instead of the literal titles.
Round 1
This round was the closest to a traditional interview.
The problem was a task-scheduling one. You're given a list of tasks and a cooldown period n: the same task type must be spaced at least n days apart between two executions, there's no restriction between different task types, you can run at most one task per day, and you're also allowed to do nothing on a given day. The question asks for the minimum number of days needed to finish all the tasks.
I initially confused it with another very similar problem (the one where you need to output the actual execution order, and the answer depends on the highest frequency) — halfway through explaining I realized that was wrong, because in this problem the order of the tasks is fixed and can't be rearranged. The interviewer said, "Right, go reread the problem."
Once I had that straight it was simple: use a hash table to record the last day each task type was executed. Walk through the tasks — if the current day is less than n+1 days after that task's last execution, jump the day straight to "last execution + n + 1," otherwise just increment the day by one. Update the hash table and continue.
After I finished writing it, what he cared about wasn't the complexity, but a handful of engineering questions:
"Is your code correct when n equals 0?" — Yes, the jump condition never fires, so it just degenerates into one task per day.
"What about an empty task list?" — Returns 0; I added an early return for that.
"What if the task ID is an arbitrary string instead of an integer?" — The hash table doesn't care about the type to begin with, so no changes needed.
"Where in this code do you think it would be easiest for someone else to introduce a bug later?" — I said it was the "+1" in n+1, because whether "spaced n days apart" includes the endpoint or not is easy to misread. I pulled it out into a clearly named variable and added a comment. He seemed satisfied with that answer.
My takeaway from this round: they don't care that much about how fast you come up with the idea, they care a lot about whether what you write can be handed off to someone else to maintain.
Round 2
This round was an implementation problem — they gave me an empty file and had me write from scratch.
Requirement: implement a sliding-window average tracker. The constructor takes a window size k, and each call to next(val) returns the average of the most recent k numbers; if there aren't k numbers yet, it averages over however many there actually are.
The core of it is a moving-average-from-a-data-stream type problem, but the whole focus here was on implementation quality.
I used a circular array plus a running sum: maintain an array of length k and a sum variable. Each time a new value comes in, subtract the old value being overwritten, add the new value, then divide by the current valid count. That makes every operation O(1) with O(k) space.
After I finished writing it, I proactively did a few things that, in hindsight, probably mattered more than the algorithm itself:
First, I wrote tests. I covered: the average when the window isn't full yet, the window exactly full, after the window has rolled over, k equal to 1, and the case where everything is negative.
Second, I brought up floating-point error. The running-sum approach drifts in precision over a long run because you keep adding and subtracting floats. I said if the input is integers, accumulate as integers and only divide at the end; if it has to be floating point and runs for a long time, you can periodically recompute the sum over the window to correct the drift. The interviewer said a lot of people don't think of that, then followed up with "what's the cost of periodically recomputing?" — I said O(k), and as long as the interval is long enough it amortizes down to nothing.
Third, in the constructor I validated that k must be greater than 0, and threw an exception if it wasn't.
The follow-up was about concurrency: what happens if multiple threads call next at the same time. I said this structure is stateful and the operations aren't atomic, so the simplest fix is locking; if you want lock-free, you'd need to maintain the write position with an atomic variable, but reading the average could then see an inconsistent intermediate state, so you'd need to explicitly document whether the structure is thread-safe rather than leaving it vague.
Round 3
Also an implementation problem, longer than the previous round.
Requirement: implement a leaderboard that supports adding a score, querying the current kth-highest score, and removing a given user. Scores keep coming in continuously.
The core of it is the kth-largest-in-a-data-stream type of problem, but with a removal operation added, so you can't just use a plain min-heap.
I first aligned with the interviewer on the assumptions: is k fixed or can it vary per query (he said fixed), how to handle ties (don't worry about tied ranks, just return the score), and whether a user could be added again (yes, treat it as an update).
My approach was to maintain a min-heap of size k holding the current top k, plus a separate structure outside the heap for the rest of the data. The problem was removal: if the removed user is in the heap, you need to pull in the largest value from outside the heap to refill it. That means the outside-the-heap part also needs to efficiently get the max.
So what I ended up using was two balanced structures (the language's built-in ordered set): one holding the top k, one holding the rest. On insert, I put the value into the appropriate side first, then did a rebalance (if the top-k side overflows, move the smallest one out; if it's short, move the largest one in from the other side). Removal works the same way. Every operation is O(log n), and querying the kth highest is just taking the minimum on the top-k side, which is O(1) or O(log n).
The interviewer asked a very practical question here: "If the same user submits a score again, will your two sets end up with stale data?" Yes. So I additionally used a hash table to record each user's current score — on update, first delete by the old score from the sets, then insert the new score. I missed this at first; it only came out because he asked.
After finishing, I also added tests, focusing on the case where "the removed user is exactly on the top-k boundary."
Round 4
This round had no coding — half of it was about my experience, half about their business.
The experience questions were: talk about a piece of code you wrote that you're most satisfied with, and why; talk about a production incident you were involved in and what you did; how do you think about testing, and under what circumstances is it acceptable not to write tests; how do you communicate with someone you disagree with in a code review.
The third question I found pretty interesting. My answer was: one-off scripts and exploratory prototypes don't need tests; but anything that will be called by others, or that will run in production, must have them. And the value of tests isn't just catching bugs — it's also pinning down the expected behavior of an interface. He followed up with "so how do you judge whether you've written enough tests?" I said I don't look at the coverage number, I look at whether the key branches and edge cases are covered — high coverage made up of tests with no assertions is meaningless.
For the business part, he talked about some of what they're working on and let me ask questions. I asked how they handle consistency issues related to money, and we talked for a bit about idempotency and reconciliation. This part wasn't evaluated, but it did show that their bar for correctness really is higher than a typical product company.
Discussion
Loading comments…