QRT in One Paragraph
Qube Research & Technologies (QRT) is a London-headquartered systematic hedge fund that spun out of Credit Suisse in 2018 and has since become one of the largest quant funds in the world. It was reported to manage roughly $38 billion by early 2026, employs more than 2,000 people across 13 offices, and trades equities, futures, FX, credit, volatility and commodities using statistical and machine-learning models. It hires quantitative researchers, quantitative developers, data engineers and software engineers, and its pay is at or near the top of the London market.
Few firms have grown this fast. QRT started as a management buyout of a Credit Suisse systematic trading business with well under $1 billion in assets. By early 2025 Bloomberg put it at about $23 billion, then about $28 billion by March 2025, and later reports put it above $35 billion. That growth is why "Qube Research and Technologies" and "QRT hedge fund" are now among the most searched fund names by quant candidates.
QRT at a Glance
| Fact | Detail |
|---|---|
| Founded | 2018, as a management buyout from Credit Suisse |
| Founders | Pierre-Yves Morlat and Laurent Laizet (both previously at Société Générale and Credit Suisse) |
| Headquarters | London |
| Offices | 13 globally at the end of 2025, including Paris, Singapore, Hong Kong, Mumbai, Zurich, New York and Houston |
| Staff | More than 2,000 (company-reported, 2025), about a third in Asia-Pacific |
| Assets | Reported at roughly $38 billion by early 2026 |
| Style | Systematic, multi-asset, heavily data- and ML-driven |
| Hiring | Quant researchers, quant developers, data engineers, software engineers, traders |
All asset figures are press reports citing people familiar with the fund. QRT does not publish AUM on its website.
What QRT Actually Does
QRT is a systematic fund: every position comes from a model. Researchers find predictive signals in market and alternative data, test them historically, and combine them into portfolios that run automatically across thousands of instruments.
The firm is diversified rather than a single-strategy shop:
- Equities - statistical arbitrage and factor strategies across global markets, including a China long-only fund that press reports put above $2 billion
- Futures and macro - systematic trend, carry and relative-value strategies across rates, FX and indices
- Commodities - a dedicated operation, including an office in Houston, reportedly building towards physical commodity trading
- Volatility and credit - options and credit strategies run on the same research platform
Reports say QRT merged its two main multi-strategy funds, Torus and Prism, into a single pool. For background on the strategy families, see our guides to statistical arbitrage, machine learning in finance and quant hedge funds.
Culture and Structure
QRT is often described as more collaborative than the multi-manager "pod shops". Rather than dozens of independent teams competing for capital, research feeds a shared platform. eFinancialCareers reported that compensation was only about 38% of revenue at QRT's UK entity in 2024, far lower than some rivals, while average UK pay was still around £722,000 per head. That combination points to a firm where the platform, not individual stars, captures most of the value.
Two other features stand out:
- Deferred compensation linked to the funds. QRT's own sustainability report says more than 40% of global staff have exposure to fund performance through its deferred compensation plan.
- An international, academic workforce. The firm reports staff of more than 85 nationalities, and recruits from universities across Europe and Asia, including campus hiring at the Indian Institutes of Technology.
Roles and What They Involve
Quantitative researcher. Designs and tests signals and portfolio models. The work is statistics and machine learning applied to noisy financial data. A strong master's or PhD in maths, statistics, physics or computer science is common, though strong undergraduates are hired too.
Quantitative developer. Implements research into production: execution systems, simulation frameworks and research libraries, mostly in Python and C++.
Data engineer. Builds the pipelines that ingest, clean and store market and alternative data. With a firm this data-heavy, this is a large and well-paid team.
Software engineer. Platform, infrastructure and tooling roles across the firm.
The QRT Interview Process
QRT's own 2026 and 2027 graduate postings describe three stages.
Stage 1: Online Application
Applications are reviewed on a rolling basis, so applying early matters. The firm says it reads answers carefully and looks for specific, thoughtful responses about your interests.
Stage 2: Technical Assessment
A timed coding challenge. Candidate reports mention Codility or HackerRank, with coding plus probability, statistics or data handling depending on the role. For research roles, reports describe applied machine learning questions - ordinary least squares, random forests, gradient boosting - at an easy-to-medium level.
Stage 3: Interviews or Assessment Centre
Interviews on site or over Microsoft Teams covering technical depth and fit. Candidates typically describe:
- A first technical interview mixing a CV or project deep-dive with probability and statistics questions (Markov chains come up often)
- A coding interview at LeetCode easy-to-medium level
- A final conversation with a senior researcher or director, part technical and part fit
Glassdoor users rate QRT interviews at about 2.9 out of 5 for difficulty, which is below the brutal top-tier prop firms but well above bank quant loops.
A shortcut worth knowing
QRT's quantitative research postings encourage candidates to enter its data challenges on the ENS Challenge Data platform, and say strong performance can lead to direct follow-up from the team. If you like competition-style problems, that is a genuine route to getting noticed.
How to Prepare
| Area | What to practise | Resources |
|---|---|---|
| Probability | Conditional probability, Markov chains, expected value | Probability interview questions |
| Statistics and ML | Regression assumptions, bias-variance, overfitting, tree ensembles | Machine learning in finance, time series analysis |
| Coding | Python data manipulation, LeetCode easy-to-medium | Python quant interview questions, Python playground |
| Projects | Be able to defend every choice in your CV project | Quant research interview questions |
The most common failure point for research candidates is the project deep-dive. Interviewers ask why you chose a model, how you validated it and how you would know if it was overfitting. Prepare those answers in advance.
Pay at QRT
QRT publishes no pay figures. Our QRT salary guide collects the public data points. The headline estimates for London:
| Role | Year 1 total (est.) |
|---|---|
| Quantitative researcher | £140,000 - £250,000 |
| Quantitative developer | £105,000 - £180,000 |
| Data engineer | £85,000 - £150,000 |
Experienced researchers who own strategies can earn well into seven figures. For comparisons, see the UK quant salary guide.
How QRT Compares
| Firm | Structure | Where it hires graduates |
|---|---|---|
| QRT | Centralised systematic platform | London, Paris, Singapore, Mumbai and others |
| G-Research | Research house for a private trading firm | London |
| Squarepoint | Systematic multi-strategy fund | London, New York, Singapore and others |
| Capital Fund Management | Academic, physics-driven systematic manager | Paris, London, New York |
| Two Sigma | Systematic, technology-led fund | New York, London |
See the QRT firm profile for a quick summary.
Compensation & recruiting notes
All figures are estimates from press reports, candidate submissions and recruiter commentary. They are not provided or endorsed by Qube Research & Technologies, and actual offers vary by role, office, year and performance. Asset figures are third-party reports. Nothing here guarantees an interview, an offer or any outcome.
Frequently Asked Questions
What is Qube Research & Technologies?
Qube Research & Technologies (QRT) is a London-headquartered systematic hedge fund founded in 2018 as a management buyout from Credit Suisse. It uses statistical and machine-learning models to trade equities, futures, FX, credit, volatility and commodities worldwide.
How big is QRT?
QRT was reported to manage roughly $38 billion by early 2026, up from about $23 billion at the start of 2025, and employs more than 2,000 people across 13 offices.
Who founded QRT?
Pierre-Yves Morlat and Laurent Laizet, who previously worked together at Société Générale and Credit Suisse, led the 2018 buyout that created QRT.
Is QRT hard to get into?
Yes, though candidates rate the interviews as less gruelling than top prop firms. Expect an online coding assessment, a probability and statistics round, a coding interview and a final conversation with a senior researcher.
Does QRT hire graduates?
Yes. QRT runs internship and graduate programmes in quantitative research, trading, development and software engineering for penultimate and final year students at bachelor's, master's and PhD level. Applications are reviewed on a rolling basis.
How much does QRT pay?
Estimated first-year total compensation for London quantitative researchers is £140,000 to £250,000, with developers and data engineers somewhat lower. See our QRT salary guide for the full breakdown.
Practise the questions Qube Research & Technologies (QRT): Careers Guide actually asks
Reading about the interview is one thing - sitting one is another. Open your free Quantt prep workspace for a real course lesson plus interview-style coding tests modelled on firms like Jane Street, Citadel, Hudson River and Optiver.
Free lesson + interview practice · No credit card required