Quantitative Developer

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

Five years of relevant experience is preferred — 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

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