Client is a small financial services start-up building technology for event-contract markets and growing our presence in Japan. Our focus is on developing a robust trading platform that combines quantitative insight with strong engineering execution, supporting fast-moving market activity across emerging exchanges.
The team is lean, collaborative and hands-on, with a strong emphasis on precision, reliability and rapid iteration [suggestion: a culture of ownership and close collaboration between engineers and researchers]. We value people who enjoy solving difficult technical problems, improving production systems and turning research ideas into working trading strategies.
We are looking for a Quantitative Developer to join a small team working on a C++/Python trading platform for event-contract markets such as Kalshi and Polymarket. In this role, you will help maintain and improve the market-making engine, implement new strategies and integrate pricing and theoretical models into production systems.
You will work across the core trading stack, including the C++20 OMS, order gateway and WebSocket/FIX infrastructure, with a strong focus on latency, throughput and reliability. The role also includes improving order placement, order state tracking, reconciliation and IPC/data feeds, as well as supporting venue integrations for current and future exchanges. In addition, you will build Python tooling for supervisors, dashboards, monitoring, rotations and analysis, and contribute tests, benchmarks and diagnostics. You will collaborate closely with senior engineers and researchers to take new ideas from concept to live trading.
Strong C++ experience, ideally C++17/20
Proficiency in Python for research and operational tooling
Experience with trading systems, market making or low-latency infrastructure
Good understanding of order books, fills, position/risk management and exchange APIs
Comfort with concurrency, networking, WebSockets, REST and preferably FIX
Ability to debug production systems with care around correctness, latency and risk
