Low-Latency Engineer
Build the systems at the centre of our trading operation. You will work on performance-critical infrastructure, exchange connectivity and the tools that let our trading teams move with confidence.
Your work.
- Develop efficient market data and order entry services in Rust or C++.
- Profile systems end to end and remove latency where it affects execution quality.
- Build safe deployment, recovery and monitoring workflows.
- Partner directly with quantitative researchers and production engineering.
Your perspective.
- Deep knowledge of systems programming, concurrency and memory management.
- Experience designing reliable networked services.
- A methodical approach to benchmarking and debugging.
- Ownership of production quality and a preference for simple, maintainable systems.
The team you’ll join.
You will work alongside colleagues who value clear thinking, useful feedback and shared ownership. Our trading, research, engineering and business teams work closely across London, Singapore and Dubai. We keep communication direct and make room for different approaches to solving a problem.
How we hire.
We start with a conversation about your experience, interests and the work you want to do. Relevant technical or practical discussions follow, giving you a clear picture of the role and the people you would work with. The process is tailored to the position, and our team will explain each step in advance.
We welcome applications from people with different backgrounds and paths into the industry. Tell us about the problems you have solved and how you think. If you need an adjustment to participate in our process, include that in your enquiry.
Good work starts here.
Think precisely.
We examine assumptions, ask direct questions and use evidence to make decisions. Clear thinking creates the foundation for useful action.
Build together.
Markets connect disciplines. So do we. Traders, engineers and researchers work closely, share context and take responsibility for collective outcomes.
Keep improving.
We learn from real results, revisit our models and invest in better tools. Small, consistent improvements compound into a meaningful advantage.