Careers
Careers at quant firms, function by function
Hedge funds, proprietary trading firms and market makers hire engineers, data and ML people, infrastructure and security specialists, finance, legal and people teams - as well as researchers and traders. Start with the work you actually do.
What careers do quant firms offer?
Quant firms hire across several distinct functions. Software engineering covers trading systems, research platforms and internal products. Data and AI covers research data platforms, market-data engineering and ML platform work. Infrastructure and reliability covers research compute, Linux, networking and observability. Security engineering protects research intellectual property and the trading path. Finance, legal, compliance, operations, commercial and people teams run the firm itself. Core quant research and trading - finding signals and running risk on them - is one specialism among these, not the only way in, and several functions require neither a PhD nor a trading background.
Start with your function
Each path leads with a written guide or a real destination, not a pay page.
Software engineering
Trading systems, research platforms, internal products and full-stack work. Five distinct engineering functions, what transfers from each background, and what firms publish about their own interviews.
Software engineering at quant firms →Data and AI
Market and research data platforms, versioned history, feature and ML platform work. Point-in-time correctness is the problem that defines the job.
Research data and ML platform engineering →Infrastructure and reliability
Research compute and scheduling, Linux and networking, observability, capacity and on-call - across a batch research estate and a live trading path with opposite objectives.
Research compute and reliability →Security engineering
Detection and security-data engineering, identity, application and infrastructure security, and incident response - where research IP and a latency-sensitive trading path pull in different directions.
Security engineering at trading firms →Quant research and trading
The core desk roles: forming and testing signals, running risk on them, pricing and modelling. Routes in, the researcher-versus-trader split, and whether a PhD is required.
How to become a quant →Business and operations
Finance, legal, compliance and risk control, trading operations, commercial, marketing, workplace and people roles at the same firms - genuine vacancies, classified as what they are.
Open business and operations roles →Or start with what you are trying to do
Understand a team
Firm profiles: strategies, functions and what each one advertises.
Prepare
Interview rounds by firm, topic and difficulty.
Compare firms
Hedge fund, prop firm or market maker: how the work genuinely differs.
Evaluate compensation
Advertised pay and modelled bands, kept separate, with the evidence attached.
Explore opportunities
Every open role at the firms we track, in any function.
Discuss a hire
Brief the desk on a seat you are opening, in any of these functions.
Career guides
What is a quant?
A quant is a specialist who uses mathematics, statistics and code to find and trade patterns in financial markets. The term covers four distinct jobs: researchers who find signals, traders who run risk on them, developers who build the systems, and analysts who price instruments and model risk.
How do you become a quant?
Most quants enter through one of four routes: a quantitative PhD into research, a strong mathematics or engineering undergraduate degree into trading, a computer science background into quant development, or a financial mathematics masters into a bank quant-analyst seat. Interview preparation in probability and programming matters more than finance knowledge.
What is the difference between a quant researcher and a quant trader?
A quant researcher finds the signal; a quant trader runs risk on it. Researchers work on longer cycles with statistical depth and are hired mostly from PhD backgrounds. Traders work in real time with immediate feedback and are hired for speed of probabilistic reasoning. Trader pay is more volatile; senior researcher pay is often higher.
Do you need a PhD to become a quant?
No, but it depends on the role. Quant research at systematic funds is overwhelmingly PhD-hired, so a doctorate is close to a requirement there. Quant trading, quant development and low-latency engineering are not PhD-gated, and many of the highest-paid people in those seats hold only an undergraduate degree.
What should a quant resume contain?
A quant resume is read for evidence, not narrative: what you measured, what you built, what it ran on and what changed as a result. One page, reverse chronological, with the quantitative content of each role stated in the first line of that role. Course lists, tool logos and objective statements displace the material a screener is looking for.
What software engineering roles do quant firms hire for, and how do you move into one?
Quant firms run several distinct engineering functions: research engineering, execution and low-latency systems, data and machine-learning platforms, infrastructure and site reliability, and full-stack work on internal products. These are different jobs with different skill profiles, not a single ladder toward research, and several firms publish role structures that make the differences explicit. Which one you should target depends on which function your existing work already resembles, and the sensible first step is to read what a specific firm says about its own teams and interview loop rather than to assume a common industry process.
What does a research data or ML platform engineer do at a quant firm?
Research data and ML platform engineers own the data a research team reasons from: ingestion of market and alternative data, the storage layer researchers read through, versioning so a result from six months ago can be reproduced, and the compute and feature tooling models are trained on. It is a platform career rather than a signal-research career: the work is judged on whether researchers can ask a question quickly and trust the answer, not on the performance of any one strategy. The distinguishing technical problem is versioned, point-in-time-correct time-series data at a scale and revision rate where a plain relational table or a folder of Parquet files starts to cost more in workarounds than it saves.
What does infrastructure and reliability engineering look like at a quant firm?
Infrastructure and reliability engineering at a quant firm covers two different workloads under one roof: a batch research estate where thousands of jobs compete for a fixed pool of compute and fairness between teams is the scheduling problem, and a live trading path where determinism and a known worst case matter more than throughput. The work includes compute scheduling, Linux and networking, observability, capacity and on-call. It is generally treated as a platform product rather than as fleet maintenance, and the teams that publish about it tend to have built their own scheduling or alerting tooling rather than adopting a default.
What is security engineering like at a trading firm or investment manager?
Security engineering at a trading firm protects two assets that behave very differently: research intellectual property, where the threat is exfiltration and insider risk, and the trading path, where availability and integrity matter more than confidentiality and where a blocking control can cost money in seconds. The work spans detection and security-data engineering, identity and access, application and infrastructure security, and incident response. It is a distinct career from infrastructure engineering, and at firms that publish about it the security data platform is built and operated like any other engineering product.
What the roles pay
Pay questions go to the salary index, where advertised figures and modelled bands are labelled separately.
Quant Researcher
Signal discovery and alpha research
Quant Trader
Running risk on systematic strategies
Quant Developer
The engineering layer beneath the strategy
Quant Analyst
Pricing, risk and model validation
HFT / Low-Latency Engineer
Nanoseconds as a competitive edge
Systematic Portfolio Manager
Owning a book and a payout formula
Software Engineer
The platform the whole firm runs on
Machine Learning Engineer
Applied learning on production signals
Data Engineer
The pipelines research cannot trade without
Data Scientist
Evidence for decisions off the trading path
Site Reliability Engineer
Trading days that do not go wrong
Network and Infrastructure Engineer
Exchange connectivity, colocation and the physical path
Security Engineer
Protecting research, code and capital
Trading Operations Analyst
Reconciliation, breaks and the daily close
Risk Manager
Independent limits, stress and exposure
Compliance Officer
Licence to trade, maintained daily
Fund Finance and Accounting
Books, treasury and the cost of carry
Technical Recruiter
Hiring is the constraint, so hiring is paid for
Strategies
High-frequency trading
High-frequency trading captures very short-lived pricing edges by being faster than competitors to observe and act. The edge is largely engineering: network path, kernel bypass, and increasingly FPGA implementation of the decision hot path.
Hires most in: Chicago · Amsterdam · New York
Market making
Market makers quote continuous two-sided prices and earn the spread while managing inventory risk. Options market making is the largest and most quantitative variant, requiring volatility modelling alongside fast systems.
Hires most in: Amsterdam · Chicago · Hong Kong · Singapore
Statistical arbitrage
Statistical arbitrage trades large baskets of related instruments on mean-reversion and relative-value relationships identified statistically. It is the classic quant research discipline and the deepest employer of doctoral researchers.
Hires most in: New York · London · Hong Kong
Systematic macro
Systematic macro takes rules-based positions across currencies, rates, commodities and equity indices based on macroeconomic and trend signals. Horizons are long, capacity is high, and London is the global centre.
Hires most in: London · New York · Singapore
Machine-learning alpha
Machine-learning driven strategies use modern statistical learning and alternative data to find non-linear structure in markets. It is the fastest-growing research discipline and competes directly with AI labs for talent.
Hires most in: New York · San Francisco · London
The Quant Career Graph →
Common routes into core quant research and trading, from physics PhD to portfolio manager, mapped step by step. One specialism among the functions above, not the only door.
Methodology
Figures are expressed as annual total compensation (base salary plus expected performance bonus) in the reference market's local currency, converted from a US dollar base. They describe typical market ranges rather than any individual offer, and exclude sign-on payments, deferred equity and carried interest. Portfolio-manager figures reflect formulaic profit-share arrangements and are therefore far more dispersed than any other role.
KnowQuantIndex 2026.1 · Updated September 2026
Modelled bands describe the market, not an individual offer. Where an employer states pay, the stated figure is used and labelled as such.
Career paths
Where a career at these firms actually goes next
Progression read from the roles firms are hiring for right now, across every function they advertise.