Quantitative Developer - Execution

METABIT ASSET MANAGEMENT PTE. LTD.; Singapore; On-site; SGD 150,000 - 210,000; Full time

Job summary

METABIT ASSET MANAGEMENT PTE. LTD. in Singapore seeks a quantitative systems engineer to build low-latency trading software and research tools for a fast-paced, research-driven environment.

You will own the full lifecycle of strategies from research to production, develop backtesting tools, and support live trading with a focus on performance and reliability.

Qualifications

• Strong Linux background and production-level C++ for low-latency components.
• Python for data analysis, rapid prototyping, and framework development.
• Solid understanding of equity and futures markets, including market microstructure and order book dynamics.
• Experience designing complex frameworks (backtesters, data pipelines, analytics engines).
• Strong mathematical literacy, statistics, and validation of execution signals.

Responsibilities

• Full lifecycle implementation of quantitative strategies from research to deployment with risk controls.
• Design and develop backtesting tools on our in-house platform for research needs.
• Own live trading processes and improve production quality and reliability.
• Collaborate with engineering on core data platforms, trading infrastructure, and distributed systems.
• Translate execution models into high-performance, low-latency code for global markets.

Skills

C++ (low-latency)

Python for data analysis

Linux environments

Equity & futures markets knowledge

System design

Statistics & data analysis

Communication & collaboration

FIX protocol

Tools

FIX protocol

Exchange APIs

Job description

Key Responsibilities

• Full-Lifecycle Implementation: Participate in the full lifecycle of quantitative strategy implementation, including research, code optimization and deployment, order execution, policy compliance, and risk control.
• Research Tooling & Backtesting: Work closely with the research team to design and develop strategy research tools on our in-house backtesting platform, tailored to their specific use cases.
• Production Ownership: Take ownership of the development and maintenance of live trading processes, continuously improving the production quality and reliability of strategies through robust technical solutions.
• Infrastructure Collaboration: Collaborate with the engineering team to shape and implement core components of our distributed systems, data platforms, and trading infrastructure-your contributions will directly impact both research and live trading performance.
• Algo Optimization: Partner with the research team to iterate on execution features, translating mathematical models and signals into high-performance, low-latency code across global equity and futures markets.

Required Qualifications & Skills

• Strong Programming Foundations: Proficient in Linux environments, Production-level experience in C++ (modern standards) for low-latency components and Python for data analysis, rapid prototyping, and framework development.
• Domain Expertise: Solid understanding of equity and futures markets , including market microstructure, order book dynamics, and electronic execution logic.
• System Design & Architecture: Experience contributing to or designing complex frameworks (e.g., backtesters, data pipelines, simulation environments, or analytics engines).
• Mathematical/Quantitative Literacy: Comfortable with statistics, data analysis, and evaluating the mathematical logic behind execution signals and performance metrics.
• Communication & Collaboration: Exceptional ability to translate concepts between highly academic researchers, strict system engineers, and fast-paced production traders.

Preferred/Nice-to-Have

• Outstanding performance in competitive programming contests such as NOI or ICPC.
• Experience with distributed systems, high-performance computing (HPC), or handling large-scale tick data.
• Familiarity with connectivity protocols (e.g., FIX, native exchange APIs).
• Publications in top-tier CS or Statistics journals/conferences.
• Award-winning participant in Kaggle machine learning competitions.
• Internship or work experience in proprietary trading firms, hedge funds, or leading tech companies.