XTX Markets hires Quantitative Researcher, Software Engineer, FPGA / Hardware Engineer and Quant Developer. Candidates report 5 to 6 interview stages depending on the track (6 distinct rounds across all tracks). Below: what each round tests and how to prepare, the path by track, the timeline, pay and a 4-week prep plan, drawn from candidate reports and XTX's own recruiting pages.
XTX Markets interview: quick answer
- StagesBy track: Quantitative Researcher: 6 · Software Engineer: 5 (6 distinct rounds across all tracks)
- Online assessment~60–120 min · timed · reportedly TestGorilla or HackerRank-style (role-dependent)
- Final roundOnsite Probability, Statistics & Linear Algebra: 60 min · whiteboard
- Pay (candidate-reported)Quantitative Researcher, New grad / PhD, first year: ~$250k – $400k (≈ £200k – £320k) total comp
- RolesQuantitative Researcher, Software Engineer, FPGA / Hardware Engineer, Quant Developer
Preparing for XTX Markets? Work the rounds in order in the free XTX Markets interview funnel.
The XTX interview process at a glance
| # | Round | Format and length | Who takes it | Prep |
|---|---|---|---|---|
| 1 | Online Assessment | ~60–120 min · timed · reportedly TestGorilla or HackerRank-style (role-dependent) | All tracks | OA guidePractice |
| 2 | Recruiter / First Technical Screen | 45–60 min · video | All tracks | GuidePractice |
| 3 | Take-Home / Technical Data Round | Multi-hour take-home or live technical · researcher-heavy | Quantitative Researcher | GuidePractice |
| 4 | Onsite Coding | 60 min · live coding (Python / C++) | All tracks | GuidePractice |
| 5 | Onsite Machine Learning / Research Deep-Dive | 60 min · whiteboard / discussion · QR & ML roles | All tracks | GuidePractice |
| 6 | Onsite Probability, Statistics & Linear Algebra | 60 min · whiteboard | All tracks | GuidePractice |
Stages are listed in the order candidates report them. Formats come from candidate reports and change between cycles, offices and teams; the "Who takes it" column shows which track each round belongs to.
How the XTX process differs by track
XTX runs separate loops for different roles. Here is the path each track reports, in order:
- Quantitative Researcher (6 stages): Online Assessment → Recruiter / First Technical Screen → Take-Home / Technical Data Round → Onsite Coding → Onsite Machine Learning / Research Deep-Dive → Onsite Probability, Statistics & Linear Algebra
- Software Engineer (5 stages): Online Assessment → Recruiter / First Technical Screen → Onsite Coding → Onsite Machine Learning / Research Deep-Dive → Onsite Probability, Statistics & Linear Algebra
Not sure which track fits you? See quant trader vs quant researcher.
Every XTX round: what it tests and how to prepare
1Online Assessment
XTX's first and most-discussed filter, widely reported as one of the hardest online assessments in quant, with pass rates often cited around 5-10% (a community estimate, not an official figure).
- Format varies by role and year and sources disagree on the platform, so prepare the TOPICS, not a tool.
- Reported versions: Researchers get a machine-learning and probability test, often with a small data-analysis part; general / quant, about 20 questions in about 60 minutes on TestGorilla (probability, linear algebra, calculus, algorithms, brain-teasers); Engineering, HackerRank-style timed coding.
- Two recurring details: no time to re-derive fundamentals, and reportedly no returning to a question once you move on. Read fully, commit, pace yourself.
- It is the single biggest gate: make MLE, OLS normal equations, ridge/lasso, PCA and the SVD, and numerically stable softmax fast and automatic rather than chasing exotic topics.
How to prepare
- Speed on fundamentals. With a tight per-question budget and no going back, you can't afford to re-derive basics under pressure. Make MLE, OLS normal equations, ridge/lasso, PCA/SVD and log-sum-exp reflexive.
- Read every question fully before answering — you reportedly can't return to it. Commit, then move on.
- Practice mixed sets. The hard part is the breadth-under-time: probability, linear algebra and ML back to back. Drill switching contexts fast.
2Recruiter / First Technical Screen
A 45-60 minute remote call after the OA, mixing a recruiter / hiring-manager component with a first technical screen.
- Interviewers are reportedly unusually civil and curious for a top quant shop, seeking the edge of your knowledge, not gotchas, yet probing whatever you claim: be precise and candid.
- Expect depth on one research project or system: the problem, why that method, how you validated it, what you would change.
- Usually with a working researcher or engineer: depth on the ML and statistics in your CV, plus live coding.
- Expect pushback even on correct answers. Refine your reasoning rather than defending it.
How to prepare
- Have a crisp research / project story. Be able to explain one piece of your work end to end — the question, the method, what you'd do differently.
- Name your assumptions. When you state a method (OLS, a classifier, a estimator), say what has to be true for it to work. XTX rewards knowing the limits.
Full Recruiter / First Technical Screen guide →Practice this round →
3Take-Home / Technical Data Round
Researchers commonly report a mid-funnel take-home: an open-ended statistical / ML problem on a dataset (model-building, data analysis, or a more theoretical ML question). Engineering tracks may get a longer technical coding exercise instead.
XTX trades tiny, fleeting edges, so conclusions must survive live data. Deliver colleague-ready work (clear structure, reproducible code, honest caveats) and expect to defend every modeling choice.
How to prepare
- Validate, don't just fit. Show an out-of-sample / cross-validation story and a baseline before any fancy model. XTX cares about the diagnostics.
- Guard against multiple testing and overfitting explicitly — say how you'd control false discoveries when you try many features.
- Write clean, reproducible code. Treat it as work you'd hand a colleague: clear structure, sane defaults, numerically stable steps.
Full Take-Home / Technical Data Round guide →Practice this round →
4Onsite Coding
Graded on correctness, edge cases, time/space complexity and, distinctively for XTX, numerical stability. Research-adjacent roles get numerical or data tasks (an estimator, a stable softmax / log-sum-exp, a clustering step, a streaming statistic) testing whether code survives floating point and large inputs. Confirm constraints, state complexity, narrate edge cases: clean, correct, explainable code beats a terse 'optimal' solution you cannot defend.
How to prepare
- Numerical stability counts. Log-sum-exp, avoiding catastrophic cancellation, divide-by-n-vs-n−1 — XTX-flavored coding rewards getting the math right, not just the algorithm.
- State complexity up front and confirm constraints before you code. Then handle edge cases out loud.
- Clean over clever. Readable, correct, well-named code beats a terse optimal solution you can't explain.
5Onsite Machine Learning / Research Deep-Dive
The round XTX is known for, setting it apart from market-making peers: a deep dive into machine learning and statistical modeling, where interviewers reportedly push one or two threads to your edge.
- Topics: regularization and bias-variance, ensembles (bagging, random forests, boosting), kernels and Gaussian processes, PCA and EM, and above all pitfalls of training on financial time series. Three topics known cold beat shallow exposure to ten.
- Admitting what you don't know reportedly helps.
How to prepare
- Know the why, not just the what. Be ready to derive (OLS normal equations, the first principal component, why L1 induces sparsity) and to justify every modeling choice.
- Bias–variance with a reason. Ridge vs. lasso, how you'd pick λ, what regularization buys you — and what breaks under collinearity or too many features.
- Showing you'd validate out-of-sample is the whole game here.
Full Onsite Machine Learning / Research Deep-Dive guide →Practice this round →
6Onsite Probability, Statistics & Linear Algebra
A super-day round on XTX's mathematical foundations: derive from first principles, explain why estimators behave as they do, and move between geometry, algebra and, when asked, a few lines of code.
- Probability and statistics: MLE and common distributional families, sampling and estimator properties, why we divide by n−1, hypothesis testing and multiple-testing corrections.
- Linear algebra: covariance and positive-semidefinite matrices, the SVD and best low-rank approximation, eigenstructure, QR or Gram-Schmidt orthogonalization.
- Set up the objective or probability space explicitly, turn the crank, then sanity-check with limits, symmetry and degenerate cases: catching your own error beats a lucky final number.
How to prepare
- Derive estimators from scratch. MLE for the common families, why we divide by n−1, the OLS estimator — set up the objective and turn the crank.
- Matrix structure is a recurring theme. PSD covariance, the SVD and best low-rank approximation, eigenvalues of structured (equicorrelation) matrices. Be fluent moving between geometry and algebra.
- Sanity-check with limits and symmetry. Catching your own error (n→∞, degenerate cases) scores better than a lucky final number.
Full Onsite Probability, Statistics & Linear Algebra guide →Practice this round →
Practice the XTX rounds in order
Each round in the XTX funnel has its own practice set, with worked solutions and progress tracking. A free account saves your progress across rounds.
A 4-week XTX prep plan
Built from the order of XTX Markets's rounds on the Quantitative Researcher path and the topics in each round's practice set. Compress it if your first interview is sooner. Other tracks can swap in their own rounds from the table above.
| When | Focus | What to do |
|---|---|---|
| Week 1 | Foundations | Review probability and expected value, the topics that dominate XTX Markets's practice sets. Work the easy and medium questions in the XTX Markets question set. |
| Week 2 | Early rounds: Online Assessment and Recruiter / First Technical Screen | Practice the screening topics under time pressure, and read the OA guide. Talk every answer through out loud; interviewers score the reasoning. |
| Week 3 | Later rounds: Take-Home / Technical Data Round, Onsite Coding, Onsite Machine Learning / Research Deep-Dive and Onsite Probability, Statistics & Linear Algebra | Move to the harder final-round topics. Rehearse quoting markets with the Make a Market game and read the market-making game guide. Do timed coding sessions in your interview language. Read how quant superdays work. |
| Week 4 | Rehearse the full loop | Run each practice round in order, redo the questions you missed, and prepare two project stories plus a specific answer to "why XTX Markets?" (see behavioral questions at trading firms). |
XTX pay
| Role | Level | Total comp | Notes |
|---|---|---|---|
| Quantitative Researcher | New grad / PhD, first year | ~$250k – $400k (≈ £200k – £320k) | Candidate self-reports for first-year researchers cluster in this band, with PhD hires toward the top. Some community guides cite grad-researcher total comp around £200k–£350k. Wide variance by team and year. |
| Software Engineer | New grad → mid (London) | ≈ £113k – £188k+ (levels.fyi, London) | levels.fyi shows a London SWE median around £188k total; new-grad packages start lower. Strong engineering comp, though researchers typically out-earn engineers at equivalent tenure. |
| Quantitative Researcher | Senior / several years (good year) | $700k – $2M+ (reported) | Senior researchers are reported to earn into seven figures, 'often substantially so' per community guides. Highly performance-tied — these are outlier-capable bands, not medians. |
Total compensation means base salary plus bonus and any sign-on, as candidates self-report it. Job postings usually show base salary only, so posted figures and outside salary pages often look lower. These are rough ranges, NOT offers or guarantees. XTX pays at the top of the London market and senior researchers reportedly reach seven figures, but comp is heavily performance- and year-dependent. Treat any single number as illustrative. Source: Candidate self-reports aggregated from levels.fyi, Glassdoor, and community threads — not employer-verified. XTX is London-based, so figures are often quoted in GBP; USD conversions are approximate. Last updated June 29, 2026.
XTX teams and what their interviews test
XTX is a single proprietary market-making firm, not a multi-strat platform with separate pods. The whole business is one research-and-engineering machine pointed at electronic liquidity provision. The interview funnel you face depends mostly on your ROLE (researcher vs. engineer vs. hardware), not a business unit.
| Business & roles | What it does | What its interviews test |
|---|---|---|
| FX market making (flagship) Quant Researcher / SWE | XTX reportedly became the largest FX spot liquidity provider in the world — its core business. Forecasts from ML models drive quoting and risk on exchanges, ECNs, and directly to clients. | Statistical forecasting, regression and ML on noisy financial data, low-latency systems thinking. The research bar is high; the engineering bar is high. |
| Equities, fixed income, commodities & crypto Quant Researcher / SWE | Beyond FX, XTX trades equities and has expanded into fixed income, commodities and (more selectively) crypto. Same model-first playbook applied across asset classes. | Cross-asset statistical reasoning, feature engineering, model diagnostics, and the practical pitfalls of training on financial time series. |
| Quantitative Research Quant Researcher (often PhD) | The heart of the firm. Building, validating and improving the predictive models that drive trading. ML-heavier than most peers, with a very large GPU training stack reported in-house. | ML theory deep-dives (regularization, ensembles, kernels, PCA/EM), statistics (MLE, multiple testing), linear algebra (SVD, covariance, eigenstructure), and a take-home / data round. |
| Software & FPGA / Hardware Engineering Software Engineer / FPGA Engineer | Low-latency trading systems, ML training and serving infrastructure, networking, and FPGA/hardware acceleration for the latency-critical paths. | Hard algorithmic coding (HackerRank-style), systems and concurrency, and — for FPGA roles — digital design, timing, and hardware fundamentals. |
About XTX
| Firm type | Electronic / algorithmic market maker (ML-driven) |
|---|---|
| Trading style | Systematic / model-driven · High-frequency to mid-frequency systematic |
| Founded | 2015 |
| Headquarters | London, UK |
| Other offices | Singapore, Mumbai, New York, Yerevan (Armenia) |
| Headcount | n/a (proprietary market maker, not an asset manager) |
| Employees | ~300 (reported, 2024–25) |
| Reported performance | Revenue reportedly ~£2.7B and profit ~£1.3B (2024, per UK filings coverage) — not an interview talking point, and figures vary by source. |
Founded 2015 by Alex Gerko
XTX Markets was founded in January 2015 by Alex Gerko, a mathematics PhD and former Deutsche Bank statistical-arbitrage trader. It grew rapidly into one of the largest electronic market makers in the world and, reportedly, the single largest FX spot liquidity provider globally (a position widely cited since around 2019). The firm is privately held and, by most accounts, took no external capital.
ML-first, research-led
XTX's edge is statistical: it forecasts short-horizon price moves with machine-learning models and quotes liquidity off those forecasts. Reports describe an unusually large in-house ML training infrastructure (a substantial GPU cluster) relative to the firm's small headcount. The interview culture mirrors this — researchers should expect genuinely deep ML and statistics questions, not surface-level trivia.
How the firm is positioned
Public coverage of XTX's UK filings has described very large revenues and profits (on the order of a couple of billion pounds of revenue and around a billion-plus in profit in recent years), making it one of the most profitable-per-head trading firms anywhere. Exact figures vary by source and year — don't quote precise numbers in an interview unless you can cite them.
Tech stack (reported)
Python and the standard scientific / ML stack for research; C++ for low-latency execution paths; FPGA / hardware for the most latency-critical components. A large GPU training cluster underpins the research side. Specifics are not publicly documented — treat this as candidate-reported, not official.
Culture
Small, elite, research-first. Headcount is deliberately tiny relative to the dollar volume traded — the firm leans hard on automation and a very large in-house ML training infrastructure. Reviews describe smart colleagues, a flat technical culture, and London-anchored hours; reports on work-life balance and onboarding are mixed and team-dependent.
What the interviews feel like
Technical, deep, and honest. Candidates report interviewers are notably civil for a top quant shop — they want to find the limit of your knowledge, not trap you. Admitting what you don't know reportedly scores better than bluffing. The notoriously hard online assessment up front is the main filter; later rounds feel more like peer discussions.
Small, elite, and team-dependent
With only a few hundred people, XTX hires very selectively and each person carries weight. Reviews skew positive on colleague quality and technical depth; experiences on pace, onboarding and work-life balance are mixed and depend on the team and role. As with any small firm, the specific team you land on matters a lot — ask about it.
Practice for XTX on QuantVault
Practice
Courses
Games and drills
Further reading
- How quant superdays work
- Market-making game interview guide
- Behavioral questions at trading firms
- C++ and low-latency interview questions
- The quant interview process, explained
Similar firms to compare
Firms with a similar business and interview style. Many candidates interview at several of them in the same cycle.
Sources and how this guide is built
- Updated October 20, 2026; firm data last reviewed June 29, 2026; pay last updated June 29, 2026.
- Round detail draws on candidate interview reports collected by QuantVault.
- XTX Markets's own pages: XTX Markets careers.
- Public candidate reports and outside interview guides are cross-checked against each other. Where they disagree the page says "reports differ"; details from a single source are marked as candidate reports.
- No actual interview questions are reproduced on this page. The questions, with worked solutions, are in the practice rounds.
XTX interview FAQ
How many rounds are in the XTX Markets interview?
It depends on the track: Quantitative Researcher: 6; Software Engineer: 5 stages. Across all tracks candidates report 6 distinct rounds: Online Assessment, Recruiter / First Technical Screen, Take-Home / Technical Data Round, Onsite Coding, Onsite Machine Learning / Research Deep-Dive, Onsite Probability, Statistics & Linear Algebra.
What happens in the XTX Markets final round?
Onsite Probability, Statistics & Linear Algebra (60 min · whiteboard). A super-day round on XTX's mathematical foundations: derive from first principles, explain why estimators behave as they do, and move between geometry, algebra and, when asked, a few lines of code.
Does the XTX Markets process differ by role?
Yes. Quantitative Researcher: Online Assessment, Recruiter / First Technical Screen, Take-Home / Technical Data Round, Onsite Coding, Onsite Machine Learning / Research Deep-Dive, Onsite Probability, Statistics & Linear Algebra. Software Engineer: Online Assessment, Recruiter / First Technical Screen, Onsite Coding, Onsite Machine Learning / Research Deep-Dive, Onsite Probability, Statistics & Linear Algebra.
How much does XTX Markets pay?
Candidate-reported total compensation: Quantitative Researcher (New grad / PhD, first year) ~$250k – $400k (≈ £200k – £320k); Software Engineer (New grad → mid (London)) ≈ £113k – £188k+ (levels.fyi, London); Quantitative Researcher (Senior / several years (good year)) $700k – $2M+ (reported). These are self-reported totals including bonus, not offers; job postings usually list base salary only.
How should I prepare for the XTX Markets interview?
Prepare round by round over about four weeks: foundations first, then the early rounds (Online Assessment, Recruiter / First Technical Screen, Take-Home / Technical Data Round), then the later rounds, then a full rehearsal. Each round has its own practice set on QuantVault, and the online assessment has a dedicated guide.
What does XTX Markets do?
XTX Markets is an electronic / algorithmic market maker headquartered in London, UK. XTX Markets was founded in January 2015 by Alex Gerko, a mathematics PhD and former Deutsche Bank statistical-arbitrage trader.
Does XTX use mental maths tests or market-making games?
No. The process focuses on machine learning, statistics, probability and coding.
How hard is the XTX online assessment?
Hard. Community estimates put the pass rate at roughly 5 to 10 percent.
Start preparing for XTX Markets
Open the interactive XTX Markets interview funnel: every stage with its practice set, from the first screen to the final round. Create a free account to save your progress.