LQT Logo

We’re growing our team.

Not rapidly, but with purpose.

Explore Opportunities

Big Ambitions, Small Team

// THE COMPANY

Founded in late 2024 by Pierre and Merlin Laffitte, LQT Technologies Limited is a software company based in London that specializes in researching and developing automated trading strategies for financial markets. We create fully systematic algorithms, license them to our partners, and support their deployment.

// TECHNICAL BOUNDARIES

Our team is united by a passion for pushing technical boundaries. We pair Mathematics with cutting-edge engineering to deliver state-of-the-art algorithmic trading strategies. We may be small, but we’re nimble and driven to compete at the highest level.

// CULTURE & MERITOCRACY

Collaboration and meritocracy define our culture. We believe in sharing information openly so everyone can perform at their best. We aim for accountability without rigid ownership or mandates, ensuring every idea can thrive.

Join Us in Building the Future

// ENGINEERING ALIGNMENT

At LQT, we’re growing deliberately, seeking individuals with world-class technical skills who want to make a genuine impact on the next generation of systematic trading technology.

// REQUISITE PROFILES

We welcome candidates with strong mathematical backgrounds who can code, or software engineers who are comfortable with advanced Maths.

// TECHNICAL OUTCOMES

Whether you love unraveling complex equations or refining low-latency systems, your expertise can help shape our cutting-edge solutions.

EVERGREEN OPPORTUNITY

Quantitative Researcher

Systematic Trading (HFT)
Location London (UK) - hybrid
Experience 0-5 years (new grad / early-career welcome)
Type Permanent, full-time
// ABOUT THE ROLE

As a Quantitative Researcher you'll work side-by-side with quantitative developers and own the path from hypothesis to live P&L. There is effectively zero red tape between research and engineering - if you want to test a new signal in the morning with an experiment, and benchmark AVX256 vs AVX512 instructions in the afternoon, we'll hand you the keys.

// WHAT MAKES THIS ROLE DIFFERENT
  • Frictionless idea → P&L: Thin wall between research and production; prototypes graduate fast to realistic sims and live trading.
  • No rigid silos: Everyone on the team contributes across math, data, and code; shared ownership of results.
  • Modern by default: Kubernetes, Python + uv, Arrow/Parquet, C++23; reproducible, containerized, observable.
  • Real hardware: HPC grid with tens of thousands of SOTA CPUs and GPUs.
// WHAT YOU'LL DO
  • Work on predictive models: Explore large/heterogeneous datasets, come up with new ideas and test them.
  • Run simulations: Design experiments to test hypotheses, code those and run them on the HPC grid.
  • Work on productionization: Translate your research ideas into clean, production-ready code and partner with others on execution, monitoring, and alerting.
  • Make it last: Implement automated sanity checks, drift detection, and anomaly investigation - garbage in, garbage out, we want some well-thought-out guardrails.
  • Research platform: Contribute to shared tooling that makes great research reproducible and fast. If you see areas of improvement, you’ll have the opportunity to suggest changes and even tackle them.
// WHAT WE'RE LOOKING FOR

Everyone on the team shares two traits: learns fast and has a strong drive. Beyond those, we look for:

  • Deep understanding of probability, statistics, and linear algebra. Ability to absorb PhD-level concepts fast.
  • Feel for ML and AI methods. Intuition and curiosity.
  • Solid grasp of algorithms, data structures, and performance optimization.
  • Strong Python for research; good engineering habits (tests, code review, CI). You have to be able to read C++ at the very least.
  • Comfort with Linux/Unix, Git, and command-line tooling; containers proficiency is a plus.
  • Experience writing unit/integration tests and using CI systems.
  • Clear, concise communication and a bias toward action.
// BONUS POINTS FOR
  • High proficiency in C++
  • Practical experience in reinforcement learning and advanced deep learning
  • Cuda, SIMD knowledge
  • Containers proficiency
// WHY JOIN US
  • High exposure with accountability: Take ideas from the whiteboard to live P&L.
  • Serious firepower: Immediate access to an HPC grid with massive CPU/GPU capacity and plenty of data.
  • Meritocratic culture: Flat, meritocratic and transparent culture: Decisions are fast and good ideas win, regardless of tenure.
  • Compensation: Competitive pay with uncapped performance bonus.
// INTERVIEW PROCESS

The process is short but intense:

01
Introductory Call
02
Technical Screen
03
On-Site Session
// HOW TO APPLY

Ready to push technical boundaries with us? We review every application personally and respond quickly.

Email us with your CV

Readings

// THE SYLLABUS

We don’t believe in gatekeeping, but we do believe in setting an exceptionally high bar. Exceptional systematic trading demands a relentless convergence of machine-level engineering, rigorous mathematics, and architectural depth. This curated syllabus maps the foundational pillars of our intellectual stack: the core concepts we benchmark against every single day. Whether you are preparing for our interview pipeline or simply pushing your own technical boundaries, this is the baseline.

I. Computer Science Fundamentals
[01]
Systems Engineering (MIT 6.033)

High-throughput distributed systems, consensus protocols, and the trade-offs of complex architectural designs.

[02]
Computer Systems (CS:APP Book)

A programmer's perspective on machine-level representation of programs, processor architecture, and memory hierarchy.

[03]
Data-Oriented Design & C++

Mike Acton's legendary talk on hardware-centric programming models, cache friendliness, and memory layouts.

[04]
Structure of Programs (CS61A)

UC Berkeley's masterclass on abstraction, programming paradigms, and managing complexity in large-scale software.

Computer Organization (CS107)

Stanford's transition syllabus from high-level languages to raw memory allocation, bits, bytes, and assembly.

[06]
Shared Environments (Farmshare)

Documentation on managing shared computing environments, CPU cycles, and optimizing multiuser systems.

II. Software Stack
[07]
Claude Code

Agentic CLI tool for rapid terminal-based software development, refactoring, and codebase navigation.

[08]
HDF5 and Parquet (Arrow)

Modern file formats and memory layouts optimized for high-performance processing of extremely large datasets.

[09]
Git Flight Rules

A practical, comprehensive guide to troubleshooting, correcting mistakes, and mastering Git under pressure.

[10]
VSCode Keyboard Shortcuts

Official cheatsheet to optimize editor workflows, maximize keyboard-only navigation, and eliminate friction.

[11]
Command-Line Mastery

Using tools like tmux, vim, and less to navigate remote environments, handle files, and use the terminal like a professional.

III. Random but Excellent Software Reads
[25]
Modern C++ in Depth

Jason Turner’s essential CppCon Talks on compiler optimizations, clean templates, execution structures, and API design.

[26]
Software Design is Knowledge Building

An insightful perspective framing software engineering not as pure coding, but as a continuous process of capturing logic and building models.

[27]
Heap Fragmentation Micro Benchmark

A rigorous deep dive into allocator dynamics, illustrating why low-latency engineering is hard and explaining hidden fragmentation traps.

IV. Prop Trading Culture & History
[28]
The Man Who Solved the Market

Gregory Zuckerman’s biography of Jim Simons, documenting the rise of Renaissance Technologies and quantitative trading paradigms.

[29]
Fortune’s Formula

William Poundstone’s narrative mapping the intersections of the Kelly Criterion, Claude Shannon, information theory, and scientific betting systems.

[30]
The Idea Factory

Jon Gertner’s account of Bell Labs, exploring the origins of the transistor, lasers, and information theory under the lens of research and creativity.

V. Low-Latency & Networking Deep Dive
[31]
Eurex HFT Overview

Exchange mechanics, structural rules, and administrative interfaces governing modern high-frequency trading behaviors.

[32]
Eurex STAC Summit Presentation

Technical engineering slides detailing how exchange matching engines optimize execution loops and reduce execution-time distribution spreads.

[33]
Stephane Tyc (McKay Brothers) Talk

Insightful YouTube presentation covering global low-latency network designs and microwave infrastructure used by HFT market-makers.

VI. Machine Learning & Research Perspectives
[34]
ML in Trading: Things to Consider

Dave Peterson’s STAC Summit presentation discussing engineering challenges of machine learning in production (many points are debatable, but remain highly interesting).

[35]
The Bitter Lesson

Rich Sutton's seminal essay explaining why computation-scale methods (search and learning) consistently outpace human-engineered heuristics over time.

[36]
Bitter Lesson (Original UT Austin Page)

The original lecture note framework hosted on the University of Texas at Austin Data Course page, tracing historical shifts in research paradigms.