Location: Shanghai
On-Site | Full-time
Compensation: $150K–$200K + equity
Our client is an innovative quantamental investment platform that empowers investors to build, share, and collaboratively execute sophisticated trading playbooks. The platform covers the entire investment lifecycle, including financial modeling, data visualization, quantitative strategy development, event-driven backtesting, and real-time dashboard monitoring. Designed to seamlessly convert strategic ideas into live execution parameters without requiring code, the platform fosters collaborative intelligence to turn individual research into a powerful, shared edge.
On behalf of this client, we are seeking a top-tier Quant Engineer to build and scale the core, production-grade trading engine that powers the platform's execution layer. This is a pure systems and infrastructure engineering position, rather than a quantitative strategy or research role. The successful candidate will focus heavily on infrastructure, architectural correctness, and high-level reliability across the end-to-end framework: from research and event-driven simulation to paper trading, live order routing, and real-time execution.
Key Responsibilities
- Core Engine Evolution: Design, build, and optimize the core execution engine, establishing robust systems for event-driven backtesting simulations, precise data alignment, and dynamic strategy runtimes.
- Live Trading Systems: Architect and implement low-latency live trading infrastructure, managing signal-to-order pipelines, order lifecycle processing, fills/partial-fills reconciliation, and direct broker or exchange integrations via REST and WebSockets.
- System Reliability: Solve critical trading engine edge cases, including state consistency across simulated, paper, and live trading environments, strict system idempotency, duplicate-order prevention, latency reduction, and slippage mitigation.
- Infrastructure Autonomy: Collaborate closely within a high-caliber, low-bureaucracy engineering unit to define and build out the execution layer for an AI-driven investment ecosystem.
- Production Trading Systems Experience: Proven professional background building, maintaining, or heavily contributing to production-grade algorithm or quantitative trading engines.
- Failure Mode Expertise: Deep, practical understanding of how distributed architectures and live market trading pipelines fail in real-world scenarios, going beyond pure theoretical simulation.
- Commitment to Correctness: Exceptional attention to detail regarding data accuracy, transactional edge cases, state consistency, and fault-tolerant software engineering where system bugs translate directly to financial anomalies.
- Technical Background (Preferred Frameworks): Familiarity with open-source or institutional trading frameworks such as Lean, NautilusTrader, or Hummingbot is highly advantageous.
- Backend Proficiency: Solid foundations in backend architecture, event-driven/distributed frameworks, or real-time streaming architectures using languages such as Python, Go, or Rust.