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- Tasks: Develop a cutting-edge Python-based research platform for quantitative trading.
- Company: Join a leading global prop trading firm shaping the future of finance.
- Benefits: Enjoy a collaborative culture, competitive salary, and opportunities for rapid growth.
- Why this job: Be part of a flat team where your contributions directly impact innovative projects.
- Qualifications: 2-10 years in high-performance environments, advanced Python skills, and strong Linux fundamentals required.
- Other info: Ideal for those eager to shape a platform from the ground up.
How many opportunities will you get to develop a greenfield platform that directly shapes the future of a multi-billion-dollar prop trading business? I'd imagine this is one of few.
Here, you'll develop a proprietary Python-based research platform from scratch for one of the leading Quantitative Traders globally. The challenge isn’t just performance or raw throughput - though both matter. It’s building a flexible, robust system capable of supporting a wide range of research workflows, each with its own quirks, data dependencies, and computational demands.
You won't just be writing clean, efficient Python, you'll be working directly with researchers to understand their methodology, tooling, and pain points. The system you help design will either accelerate the productivity of some of the smartest minds in quantitative finance - or get in their way.
There’s no single background that guarantees success here, but mastery of Python, a deep understanding of system design, and a collaborative mindset are non-negotiable. While most of the team comes from big-tech, there's space for exceptional buy-siders too.
Here, culture matters. This is a flat, collaborative team with no space for ego. They move fast, build carefully, and share success (and failures) together. If you're looking for a heads-down, siloed role, this isn't the right fit. If you want to be involved from the beginning - shaping how this platform evolves from scratch - now is the time to reach out.
What they’re looking for:
- 2–10 years of experience in a high-performance, engineering-driven environment
- Advanced Python proficiency
- Experience designing and building complex, data-intensive systems
- Strong Linux fundamentals
- Excellent communication skills and a comfort working closely with non-engineering stakeholders
Great to have: experience with containerization (e.g., Docker, K8,) and exposure to Golang.
If this sounds interesting - or if you're simply curious to learn more - reach out for a conversation. The team is actively hiring and will move quickly for the right person.
Software Developer (Research Infrastructure) - £350/500,000 - Quantitative Trading employer: Campbell North
Contact Detail:
Campbell North Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Software Developer (Research Infrastructure) - £350/500,000 - Quantitative Trading
✨Tip Number 1
Familiarise yourself with the latest trends in quantitative trading and research infrastructure. Understanding the current landscape will help you engage in meaningful conversations during interviews and demonstrate your genuine interest in the field.
✨Tip Number 2
Brush up on your Python skills, especially focusing on performance optimisation and system design. Being able to discuss specific projects where you've implemented these skills can set you apart from other candidates.
✨Tip Number 3
Prepare to showcase your collaborative mindset. Think of examples where you've worked closely with non-engineering stakeholders, as this role requires excellent communication and teamwork skills.
✨Tip Number 4
If you have experience with containerization tools like Docker or Kubernetes, be ready to discuss how you've used them in past projects. This knowledge could give you an edge, as it's a great asset for the role.
We think you need these skills to ace Software Developer (Research Infrastructure) - £350/500,000 - Quantitative Trading
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your Python proficiency and experience in building complex systems. Include specific projects or roles that demonstrate your ability to work in high-performance environments.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for developing a greenfield platform. Mention your collaborative mindset and how you can contribute to the team culture, as well as any relevant experience with non-engineering stakeholders.
Showcase Relevant Projects: If you've worked on projects involving containerization or Golang, be sure to mention these. Provide examples of how you've tackled challenges in system design and data management.
Prepare for Technical Questions: Anticipate technical questions related to Python, system design, and Linux fundamentals. Be ready to discuss your thought process and problem-solving approach during interviews, as communication skills are key.
How to prepare for a job interview at Campbell North
✨Showcase Your Python Mastery
Make sure to highlight your advanced Python skills during the interview. Be prepared to discuss specific projects where you've used Python to solve complex problems, and consider bringing examples of your code or contributions to open-source projects.
✨Demonstrate System Design Knowledge
Since the role involves building complex, data-intensive systems, be ready to talk about your experience with system design. Discuss any relevant projects where you designed systems from scratch, focusing on how you addressed performance and scalability challenges.
✨Emphasise Collaboration Skills
This team values a collaborative mindset, so be sure to share examples of how you've worked effectively with non-engineering stakeholders. Highlight any experiences where you gathered requirements or feedback from researchers or other teams to improve a project.
✨Prepare for Technical Questions
Expect technical questions that assess your understanding of Linux fundamentals and system architecture. Brush up on key concepts and be ready to explain how they apply to the development of high-performance research platforms.
