About Qube Research and Technologies
Qube Research and Technologies (QRT) is a London- and Hong Kong–based systematic hedge fund that grew out of the BNP Paribas systematic group before spinning out as an independent firm. QRT trades a wide range of asset classes — equities, futures, options, and credit — with a heavy emphasis on machine learning research and large-scale data infrastructure. The firm has scaled rapidly and is among the most active sponsors of European quant talent.
For current openings see our QRT listings.
Interview Process
- Online assessment: probability + coding test, timed
- First-round interview: probability and a coding problem
- Final round: three to five rounds covering ML, statistics, coding, and behavioral fit
Final-round timelines have been reported as fast — often within two weeks of the first interview.
For Quantitative Researcher Roles
QRT's QR interview leans heavily ML-flavored. Expect questions like:
- How do you regularize a high-dimensional regression?
- How do you validate a model when you have very limited data?
- Describe an ML project you led; what worked, what didn't, what would you change?
- Probability and statistics — Bayes' theorem, MLE, hypothesis testing
- Time series — autocorrelation, stationarity, regime detection
QRT cares about applied judgment — they want to know if you can actually do research, not just recite definitions.
For Quantitative Developer / SWE Roles
Engineering interviews focus on:
- Python and C++ coding
- Data infrastructure — building pipelines that move terabytes daily
- System design with low-latency or high-throughput constraints
- Discussion of past projects with measurable impact
Coding
The coding round is more "build something realistic" than LeetCode tricks. Practice writing correct, readable Python that handles edge cases well. For C++ roles, modern features (smart pointers, move semantics, concurrency primitives) are fair game.
Behavioral
QRT is research-driven and meritocratic. Interviewers ask about how you've collaborated with PMs / traders / engineers, how you've responded to a research project that didn't pan out, and what you'd want to work on if you joined.
How to Prepare
- Refresh your ML fundamentals — bias-variance, regularization, cross-validation
- Have one substantive research project you can describe end-to-end
- Practice writing clean, idiomatic Python under time pressure
- For developer roles: study modern C++ and at least one large-scale data system in depth