Quant Developer: Scale Trading Systems & ML

G-Research

Greater London

On-site

GBP 90,000 - 130,000

Full time

2 days ago
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Benefits offered by this job

Annual discretionary bonus
Lunch provided
35 days annual leave
9% pension contributions
Casual dress code
Health and life cover

Job summary

G-Research is hiring a Quantitative Developer to join our London-based engineering team. You will shape platforms and tools that empower researchers, building scalable systems in C# and Python within our own data centres.

The role emphasizes end-to-end ownership and rigorous software engineering. You will work alongside researchers on trading, modelling, simulation, and tooling, delivering high-quality, maintainable code and contributing to performance optimisations and large-scale distributed

Qualifications

  • Deliver high-quality, well-engineered software with strong architectural awareness.
  • Take end-to-end ownership of solutions, from concept to delivery.
  • Demonstrate solid knowledge of algorithms, data structures, and software fundamentals.
  • Show interest in quantitative finance and the role of engineering within it.

Responsibilities

  • Design and build scalable trading and research platforms.
  • Collaborate with researchers to turn ideas into production systems.
  • Improve performance and reliability of core tech stacks (C# and Python).
  • Champion engineering best practices and scalable improvements.

Skills

Software engineering
Architectural awareness
End-to-end ownership
Algorithms and data structures
Communication

Tools

C#
Python

Job description

G-Research is hiring a Quantitative Developer to join our London-based engineering team. You will shape platforms and tools that empower researchers, building scalable systems in C# and Python within our own data centres.

The role emphasizes end-to-end ownership and rigorous software engineering. You will work alongside researchers on trading, modelling, simulation, and tooling, delivering high-quality, maintainable code and contributing to performance optimisations and large-scale distributed

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