Quant Performance Engineer
Posted Sep 30, 2026 · We last checked this listing on Oct 6, 2026
Likely interview questions for this role
Written from this job description, not a generic list. Each one notes what the interviewer is really checking.
About your past
Tell me about a time you took apart a system or tool to understand it better than its documentation did.
What they are checking: Curiosity that goes past the manual, which is what reverse-engineering exchange and network gear really demands.
How have you used an LLM to speed up a data or research task, and where did you stop trusting its output?
What they are checking: Practical judgment about frontier models, including knowing where they fail and how to check them.
Tell me about a competition, project or game where you were trying hard to win. What did you do that others didn't?
What they are checking: Real competitive drive and an edge-seeking mindset, which this kind of trading job depends on.
What would you do
Your analysis says a change should save 200 nanoseconds, but in production nothing improves. What do you look at first?
What they are checking: Whether they suspect their own measurement and assumptions before blaming the system or the implementers.
A hardware engineer disagrees with your idea and says it can't be built in time. How do you handle that conversation?
What they are checking: Whether they can collaborate with implementers, take constraints seriously and still push a good idea forward.
About the work
If two exchanges both claim the same matching engine latency, how would you design an experiment to find out which one is actually faster for us?
What they are checking: Whether they can turn a vague claim into a measurable test and control for noise like network jitter and clock drift.
Walk me through how you'd measure the time between a market data packet hitting our network card and our order leaving the box.
What they are checking: Whether they understand hardware timestamps, clock sync and where software timing quietly lies to you.
You have a dataset of a billion order events and a hunch about queue position. How do you go from that hunch to something a trader can use?
What they are checking: Whether they can move from raw data to a useful conclusion without drowning in the size of the dataset.
How would you build an ETL pipeline in Python for exchange packet captures that you can trust a month from now?
What they are checking: Whether they think about schema drift, validation and reproducibility, not just getting the first run to work.
Explain the difference between taking liquidity and quoting, and why latency matters differently for each.
What they are checking: Basic market microstructure sense, which tells us if they can reason about strategy and not only code.
If I ask you to estimate how many messages per second a busy options exchange sends at the open, how would you reason it out?
What they are checking: Comfort with fast, structured estimation under uncertainty, which mirrors how trading problems get scoped.
Practice this interview out loud.
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Practice this out loudThe full job description
As published by IMC Trading.