Location: ShanghaiOn-Site | Full-timeCompensation: $150K–$200K + equityOur 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 ResponsibilitiesCore 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.RequirementsProduction 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.BenefitsCompetitive base salary ranging from $150,000 to $200,000 per annum, commensurate with experience.Meaningful equity packages (stock options) providing significant long-term ownership upside.Opportunity to work within a highly autonomous, small, and elite team environment with minimized bureaucracy.High-impact role defining the architectural baseline of a next-generation collaborative investing platform.Due to the high volume of applications we anticipate, we regret that we are unable to provide individual feedback to all candidates. If you do not hear back from us within 4 weeks of your application, please assume that you have not been successful on this occasion. We genuinely appreciate your interest and wish you the best in your job search.Commitment to Equality and Accessibility:At MLabs, we are committed to offer equal opportunities to all candidates. We ensure no discrimination, accessible job adverts, and providing information in accessible formats. Our goal is to foster a diverse, inclusive workplace with equal opportunities for all. If you need any reasonable adjustments during any part of the hiring process or you would like to see the job-advert in an accessible format please let us know at the earliest opportunity by emailing human-resources@mlabs.city.MLabs Ltd collects and processes the personal information you provide such as your contact details, work history, resume, and other relevant data for recruitment purposes only. This information is managed securely in accordance with MLabs Ltd’s Privacy Policy and Information Security Policy, and in compliance with applicable data protection laws. Your data may be shared only with clients and trusted partners where necessary for recruitment purposes. You may request the deletion of your data or withdraw your consent at any time by contacting legal@mlabs.city.
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.
Requirements
- 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.
Benefits
- Competitive base salary ranging from $150,000 to $200,000 per annum, commensurate with experience.
- Meaningful equity packages (stock options) providing significant long-term ownership upside.
- Opportunity to work within a highly autonomous, small, and elite team environment with minimized bureaucracy.
- High-impact role defining the architectural baseline of a next-generation collaborative investing platform.
Due to the high volume of applications we anticipate, we regret that we are unable to provide individual feedback to all candidates. If you do not hear back from us within 4 weeks of your application, please assume that you have not been successful on this occasion. We genuinely appreciate your interest and wish you the best in your job search.
Commitment to Equality and Accessibility:
At MLabs, we are committed to offer equal opportunities to all candidates. We ensure no discrimination, accessible job adverts, and providing information in accessible formats. Our goal is to foster a diverse, inclusive workplace with equal opportunities for all. If you need any reasonable adjustments during any part of the hiring process or you would like to see the job-advert in an accessible format please let us know at the earliest opportunity by emailing human-resources@mlabs.city.
MLabs Ltd collects and processes the personal information you provide such as your contact details, work history, resume, and other relevant data for recruitment purposes only. This information is managed securely in accordance with MLabs Ltd’s Privacy Policy and Information Security Policy, and in compliance with applicable data protection laws. Your data may be shared only with clients and trusted partners where necessary for recruitment purposes. You may request the deletion of your data or withdraw your consent at any time by contacting legal@mlabs.city.
We are a Haskell, Rust, Blockchain, AI & Recruitment consultancy.
We are a Haskell, Rust, Blockchain, AI & Recruitment consultancy.
