Key Responsibilities
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Research and implement predictive alpha signals using tick-level order book data, trade imbalance metrics, and alternative data streams
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Optimize latency-critical trading strategies through FPGA-aware algorithm design and kernel-level performance tuning
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Conduct rigorous post-trade analysis using our proprietary simulation framework (1ns timestamp precision)
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Collaborate with core infrastructure team to push execution latency below 500 nanoseconds
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Continuously monitor and adapt to evolving market microstructure patterns (e.g., periodic auction mechanisms, dark pool liquidity)
Requirements -
PhD/MS in Physics/CS/Mathematics from TOP 10 global institutions (e.g., MIT, Stanford, Tsinghua, IIT)
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3+ years experience developing production HFT strategies (NOT backtest-only research)
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Mastery of:
▶️ Modern C++17/20 (template metaprogramming, lock-free data structures)
▶️ Python quantitative stack (NumPy, pandas, JAX)
▶️ Linux kernel tuning & network stack optimization -
Deep intuition for order book dynamics and exchange matching engine behaviors
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Obsession with nanosecond-level optimizations (cache locality, branch prediction, etc.)