Quantitative Developer

Role Overview

We are seeking a Quantitative Developer to join our team in Singapore. You will convert research prototypes into robust Rust-based production systems, including quoting, hedging, and inventory management logic, complemented by thorough testing. Additionally, you will build and sustain a shared backtesting and simulation framework that accurately models market dynamics such as queue position, latency, fees, and market impact.

Key Duties

• Transform research algorithms into production-quality Rust code encompassing quoting, hedging, and inventory functions with comprehensive testing including edge scenarios.
• Develop and maintain a collaborative backtesting and simulation platform that reflects realistic market conditions.
• Construct signal and feature pipelines ensuring consistency between offline research outputs and live production data to maintain integrity of trading signals.
• Create tools for P&L attribution and execution quality analysis utilized by the trading team, including markout and adverse-selection metrics.
• Manage configuration and parameterization systems that enable safe, auditable, and reversible adjustments to live strategies.
• Identify and analyze unexpected behaviors in strategies by isolating causes related to models, parameters, market regimes, or trading venues.
• Enhance research and trading efficiency by automating manual processes that cause delays.
• Integrate AI-assisted coding practices to speed up development cycles and improve code quality.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Mathematics, Physics, Statistics, or other related STEM disciplines.
• Up to five years of relevant experience; strong recent graduates are welcome, and domain-specific skills will be cultivated internally.
• Proficient programming skills in Rust, or strong experience in C++, Go, or Java with capability to quickly learn Rust.
• Fluency in Python for research and data analysis with experience in libraries such as pandas (or polars) and numpy.
• Solid foundation in probability, statistics, and time-series analysis sufficient to evaluate the robustness of backtest results.
• Demonstrated genuine interest in market microstructure concepts such as order book dynamics, execution quality, and costs.
• High attention to detail with an aptitude for identifying subtle numerical errors that may not trigger exceptions.
• Proven ability to complete work independently, demonstrated through production code, open-source contributions, or competitive programming achievements; comfortable working autonomously and iterating quickly in a compact team setting.

Preferred Experience

• Prior engineering support experience with trading desks, research teams, or market-making operations across any asset class.
• Hands-on experience with cryptocurrency trading, whether professional or personal.
• Knowledge of perpetual funding mechanisms, basis trades, and cross-venue pricing strategies.
• Understanding of decentralized exchange mechanics, including AMMs, concentrated liquidity pools, EVM, and MEV considerations.
• Background in developing or maintaining backtesting or simulation frameworks that are used across teams.