Quantitative Developer - Python

Selby Jennings; City Of London; On-site; GBP 110,000 - 170,000; Full time

Job summary

Selby Jennings is recruiting for a Quantitative Software Engineer / Quantitative Developer for a leading global hedge fund in London. The role is hands-on, building and scaling the technology platform underpinning systematic investment strategies, working with Quant Researchers, PMs and Strategists.

You will design and enhance the trading platform, develop back-testing and feature engineering tooling, build market data infrastructure for real-time and historical tick data, and productionise

Qualifications

• 5+ years of experience as a Quantitative Developer or Software Engineer in a trading environment.
• Strong Python development and software engineering fundamentals.
• Experience building platforms that support quantitative research and trading workflows.
• Understanding of systematic investment strategies and research methodologies.

Responsibilities

• Design, build and enhance the quantitative trading platform used across research and production environments.
• Partner with Quant Researchers and Portfolio Managers to productionise systematic trading strategies.
• Develop and maintain back-testing frameworks, alpha generation pipelines and research tooling.
• Build scalable market data infrastructure for real-time and historical tick data.
• Develop execution systems and supporting trading infrastructure.
• Improve research workflows by creating robust tooling to accelerate strategy development.

Skills

Python development

Software engineering

Quantitative workflows

Tools

Backtesting frameworks

Real-time data processing

Job description

Our client, a leading global hedge fund, is seeking a highly motivated Quantitative Software Engineer / Quantitative Developer to join their Systematic Strategies team in London. This is a hands‑on engineering role focused on building and scaling the technology platform that underpins the firm's systematic investment strategies.

Working directly alongside Quantitative Researchers, Portfolio Managers and Strategists, you will be responsible for developing the infrastructure, tooling and production systems that support the full life-cycle of systematic trading strategies. The ideal candidate will have a strong engineering mindset, a deep understanding of quantitative research workflows, and experience building robust, scalable systems used in systematic trading environments.

This role offers the opportunity to work on a broad range of challenges spanning quantitative research infrastructure, machine learning platforms, market data systems, backtesting frameworks, execution technology and real-time analytics.

Responsibilities

• Design, build and enhance the quantitative trading platform used across research and production environments.
• Partner closely with Quantitative Researchers and Portfolio Managers to productionise systematic trading strategies.
• Develop and maintain core components including back‑testing frameworks, alpha generation pipelines, feature engineering platforms and research tooling.
• Build scalable market data infrastructure capable of processing and analysing large volumes of real‑time and historical tick data.
• Develop execution systems, OMS components and supporting trading infrastructure.
• Improve research workflows by creating robust tools, libraries and frameworks that accelerate strategy development and deployment.
  

Requirements

• 5+ years of experience as a Quantitative Developer, Quantitative Engineer or Software Engineer working directly with quantitative research teams within a systematic trading, quantitative investment or hedge fund environment.
• Strong software engineering fundamentals and advanced Python development experience.
• Proven experience building technology platforms that support quantitative research and systematic trading workflows.
• Strong understanding of systematic investment strategies and quantitative research methodologies.
• Ability to review, optimise and productionise research code while understanding the underlying investment rationale.
• Experience deploying machine learning models and supporting end‑to‑end production workflows.
• Exposure to AI tooling, LLM‑based workflows, or the development of AI‑enabled solutions.