Quantitative Developer

HashKey Capital; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

HashKey Capital in Singapore is looking for a software engineer to turn research models and backtests into production Rust code, including tests and edge cases a prototype skips. You will build and maintain the shared backtesting framework with latency-aware logic and market impact, and develop signal pipelines that align offline and online results.

You will own P&L attribution tooling and configuration systems to keep live strategies safe and reversible, while diagnosing unexpected behavior and

Qualifications

• Bachelor's or Master's degree in Computer Science, Mathematics, Physics, Statistics, or a related STEM field.
• Zero to five years of relevant experience — strong graduates are in scope, and we expect to teach the domain.
• Strong programming ability in Rust, or in C++, Go or Java with the demonstrated ability to pick up Rust quickly.
• Proficiency in Python for research and analysis, including the data stack (pandas or polars, numpy).
• A working grounding in probability, statistics and time-series analysis — enough to assess whether a backtest result is robust.
• Genuine curiosity about market microstructure: order book dynamics, fill quality and execution cost.
• Attention to correctness and failure cases, including the instinct to catch a wrong number that does not raise an error.
• A track record of finishing work independently — production experience, open-source contributions, or competitive programming results — and comfort with autonomy and rapid iteration in a small team.

Responsibilities

• Turn research models and prototypes into production Rust, including tests and edge cases a prototype skips.
• Build and maintain the shared backtesting and simulation framework, with fill logic that reflects queue position, latency, fees and market impact.
• Develop signal and feature pipelines that compute consistently offline and online, so a signal means the same thing in research as in production.
• Build the P&L attribution and execution-quality tooling the trading team relies on, including markout and adverse-selection analysis.
• Build and maintain the configuration and parameter systems that make changes to live strategies safe, reviewable and reversible.
• Diagnose unexpected strategy behavior — reproducing it and narrowing it to model, parameters, market regime or venue.
• Improve research and trading velocity by removing the manual steps that slow the team down.
• Actively leverage AI coding as a core part of the development workflow to accelerate delivery and improve code quality.

Skills

Rust

C++

Go

Java

Python

Pandas/Polars

NumPy

Time-series analysis

Statistics

Probability

Market microstructure

Autonomy

Failure mode awareness

Education

Bachelor's or Master's in CS/Math/Physics/Statistics

Tools

Python data stack (Pandas/Polars, NumPy)

Backtesting frameworks

Job description

• Turn research models and prototypes into production Rust — quoting, hedging and inventory logic — including the tests and edge cases a prototype skips
• Build and maintain the shared backtesting and simulation framework, with fill logic that reflects queue position, latency, fees and market impact
• Develop signal and feature pipelines that compute consistently offline and online, so a signal means the same thing in research as in production
• Build the P&L attribution and execution-quality tooling the trading team relies on, including markout and adverse-selection analysis
• Build and maintain the configuration and parameter systems that make changes to live strategies safe, reviewable and reversible
• Diagnose unexpected strategy behavior — reproducing it and narrowing it to model, parameters, market regime or venue
• Improve research and trading velocity by removing the manual steps that slow the team down
• Actively leverage AI coding as a core part of the development workflow to accelerate delivery and improve code quality

Qualifications:

• Bachelor's or Master's degree in Computer Science, Mathematics, Physics, Statistics, or a related STEM field
• Zero to five years of relevant experience — strong graduates are in scope, and we expect to teach the domain
• Strong programming ability in Rust, or in C++, Go or Java with the demonstrated ability to pick up Rust quickly
• Proficiency in Python for research and analysis, including the data stack (pandas or polars, numpy)
• A working grounding in probability, statistics and time-series analysis — enough to assess whether a backtest result is robust
• Genuine curiosity about market microstructure: order book dynamics, fill quality and execution cost
• Attention to correctness and failure cases, including the instinct to catch a wrong number that does not raise an error
• A track record of finishing work independently — production experience, open-source contributions, or competitive programming results — and comfort with autonomy and rapid iteration in a small team

Preferred:

• Experience supporting a trading desk, research team, or market-making book as an engineer, in any asset class
• Hands-on crypto trading experience, professional or personal
• Familiarity with perpetual funding, basis and cross-venue pricing
• Familiarity with DEX mechanics — AMM and concentrated-liquidity design, EVM, MEV
• Experience building or maintaining backtesting or simulation frameworks used by others