Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Quantitative Developer - High-Frequency Trading Firm

Venture Search
London
6 months ago
Applications closed

Related Jobs

View all jobs

Quantitative Developer

Quantitative Developer

Quantitative Developer

Quantitative Developer (C#) - Up to 160k + Exceptional Bonus - Elite FinTech Firm- London - Hybrid Working

Quantitative Developer - eFX (Java)

Quantitative Developer - (Python | Equities | Backtesting) Key Skills: Python, Equities, Backtesting

Quant Developer

Leading Prop Trading Firm

London


Our client, a leading prop trading firm based in London, are looking for a Quant Developer to join their high-performance team.


As a quantitative developer, your aim is to improve the firm's trading stack in any way that will make its trading strategies more competitive and profitable. In a high-frequency trading environment, this often means improving the end-to-end latency of the trading platform or increasing the scalability and precision of execution of the trading strategies.


Requirements


Responsibilities


Your main responsibilities are developing and rapidly evolving their main software components:

  • Develop low latency trading engine and strategy runtime
  • Develop market data distribution platform (internal binary protocols)
  • Develop and maintain exchange API connectivity and robust exchange connectors
  • Full automation around deployment and monitoring of a 24/7 trading system
  • Continuous profiling of trading system and strategy latency
  • Understand and reverse engineer exchange architectures


You will be part of a small development team that shares the responsibility of the whole trading stack. As you own the code, deployment and all tooling, you can rapidly and safely iterate on changes to the trading software.


Developers collaborate directly with traders and researchers, allowing for immediate reaction to market changes and fast iteration of live trading engines


Skill


  • Experience writing low-latency Java / C++ applications and architectures. HFT industry preferred but telecom and gaming industry experience also welcome
  • Ability to get the best performance out of application and networking stack of on-premise and cloud environment
  • Ability to benchmark, profile and trace full applications on Linux
  • Ability to find and resolve latency and throughput bottlenecks
  • Excited to pick up new skills to solve difficult problems (examples: eBPF, XDP, Intel PT)
  • The ideal profile has experience in the HFT industry combining software development and networking skills (TCP / UDP / multicast / WebSocket / HTTP).
  • While the firm is language agnostic, their current trading stack is mostly written in Java.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Neurodiversity in Data Science Careers: Turning Different Thinking into a Superpower

Data science is all about turning messy, real-world information into decisions, products & insights. It sits at the crossroads of maths, coding, business & communication – which means it needs people who see patterns, ask unusual questions & challenge assumptions. That makes data science a natural fit for many neurodivergent people, including those with ADHD, autism & dyslexia. If you’re neurodivergent & thinking about a data science career, you might have heard comments like “you’re too distracted for complex analysis”, “too literal for stakeholder work” or “too disorganised for large projects”. In reality, the same traits that can make traditional environments difficult often line up beautifully with data science work. This guide is written for data science job seekers in the UK. We’ll explore: What neurodiversity means in a data science context How ADHD, autism & dyslexia strengths map to common data science roles Practical workplace adjustments you can request under UK law How to talk about your neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in data science – & how to turn “different thinking” into a real career advantage.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.