Quantitative Developer

Key Responsibilities:

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.