The Qube Research and Technologies Interview Process

QRT hires out of London and Paris on parallel tracks, and the process leans harder on statistics than on the trading games US firms favour.

Qube Research and Technologies, usually shortened to QRT, hires quantitative researchers and engineers through a four to five round process split across its London and Paris offices, and the two locations run largely independent pipelines rather than a single centralised loop. A candidate applying to the Paris office should expect at least one interview conducted partly in French for certain client-facing or infrastructure roles, though core research interviews are conducted in English at both sites.

QRT grew out of systematic trading desks with roots at Credit Suisse and folded in the technology and research groups of what was previously Ako Capital, and the firm's culture still reflects that dual heritage: London handles a large share of the systematic equity and futures research, while Paris carries significant weight in infrastructure and some research functions. A candidate targeting Paris should expect the process to move slightly faster, generally three to four weeks end to end, than the London loop, which more often runs five to six.

Right to work matters differently at each site. London requires a UK Skilled Worker visa sponsorship for non-UK, non-Irish nationals, while Paris requires either an EU passport or a French or EU work authorization, and QRT sponsors both routes for candidates it wants to hire, but a candidate should raise the question directly with the recruiter rather than assume parity between the two offices.

The process opens with a recruiter call, followed by an online technical assessment covering probability, linear algebra and a coding exercise, typically in Python. The bar on the coding section is closer to what a systematic hedge fund expects than what a market maker expects: clean, correct code that handles edge cases matters more than raw speed, since the researcher role at QRT involves building and maintaining production research pipelines rather than pricing options against a clock.

Candidates who have not written production-quality Python in a while benefit from timed review on practice before sitting this stage, since the assessment penalizes code that works on the happy path but breaks on malformed input.