Career Description

We’re hiring an Algorithmic Trader focused on CME equity index futures. You’ll build and run short-horizon strategies at microsecond-to-second timescales: turning full-depth order book data into predictive signals, designing passive market-making logic for ES, and working with our C++ and FPGA developers to get your ideas into production. This role suits someone who has traded or researched ES/NQ in a low-latency environment and enjoys taking an idea from raw market data to live trading. 

What you will be doing

• Research and build short-horizon (microsecond to second) price-prediction signals for CME equity index futures from full-depth order book and trade data: order aggressiveness, queue position, cancels, icebergs, and leads from related futures and cash equities.
• Own the signal pipeline end to end: packet-capture data, feature design, sampling, model fitting (linear and tree-based), pruning, out-of-sample simulation, and deployment with C++ and FPGA developers.
• Design and tune a passive ES market-making strategy: level joining, queue-priority value, order spacing, cancel/replace timing, exchange messaging limits, and protective pulls when latency or connectivity degrades.
• Model the matching engine: turn fill latency and book state into estimates of how many levels traded and future price changes, then use this information to move quotes, purge, and take liquidity in futures, index options, and equities.
• Measure and improve latency: profile order paths in microseconds, reconcile wireless and fiber timestamps across data centers, compare vendor signal feeds, and quantify misses against exchange reports.
• Write specs for developers, verify their output against your own calculations, and find bugs in production data and pricing pipelines.

Skills and experience

• 2+ years trading or researching CME equity index futures (ES/NQ) at a proprietary trading firm with sub-millisecond systems.
• Hands-on experience with CME MDP 3.0 order-by-order market data: book building, queue-position tracking, iceberg and self-match-prevention inference, matching-engine latency behavior.
• Built microprice or alpha models that ran live at microsecond horizons. Fluent in lead/lag analysis, half-lives, markouts, and adverse selection.
• Passive quoting experience on FIFO futures markets: queue value, join and cancel logic, messaging-efficiency constraints, mass-quote protections.
• Python research stack (polars or pandas, numpy, gradient-boosted trees, SQL). Reads C++. Has worked alongside FPGA or low-latency engineers.
• Bonus: index basis and roll (cash, futures, BTIC), SPX options, or microwave network latency.