Job Description
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
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