Quantitative Software Engineer - Elite Systematic ...

An industry-leading systematic hedge fund is seeking exceptional quantitative software engineers to join its New York team. This firm operates at the cutting edge of high-performance computing, AI-driven trading strategies, and ultra-low latency execution, competing at the highest levels of global financial markets.This opportunity is tailored for individuals with extraordinary technical ability, particularly those with backgrounds in high-frequency trading, AI research labs (DeepMind, OpenAI, FAIR), or advanced computational engineering teams at organizations like Google Brain, Nvidia, or SpaceX.The FirmThis systematic trading firm has built one of the most sophisticated technology infrastructures in the financial industry, enabling real-time decision-making at an unprecedented scale. The environment is highly selective and research-driven, where engineers and quantitative researchers work collaboratively to solve some of the most complex computational problems in finance.The team focuses on ultra-low latency architectures, self-learning trading models, and high-performance execution systems. With an approach that merges machine learning, distributed computing, and hardware acceleration, this firm attracts the world's best engineers and researchers who thrive in a culture of intellectual rigor and deep problem-solving.Key ResponsibilitiesArchitect and optimize real-time execution systems that operate at the absolute limits of computational efficiency.Develop self-learning trading algorithms that evolve autonomously in live environments.Engineer customized infrastructure, leveraging FPGA acceleration, GPU-based reinforcement learning, and high-performance C++/Rust systems.Build bespoke compiler optimizations and low-level software that interacts directly with hardware.Optimize microsecond-level network performance, ensuring the fastest possible signal processing and execution speeds.Work in a highly autonomous environment, where direct contributions impact the firm's trading performance.Technology StackThe firm operates on a best-in-class technology stack, incorporating:Programming Languages: C++, Rust, Python, CUDAHardware Acceleration: FPGA, custom ASICs, GPU-driven MLMachine Learning & AI: Reinforcement learning, adversarial models, real-time signal processingDistributed Computing: High-throughput data pipelines, custom real-time databases, parallelized computingLow-Latency Systems: OS bypass, direct memory access, kernel-level optimizationsIdeal Candidate ProfileA PhD or Master's degree in Computer Science, Mathematics, Physics, or a related quantitative field from a top-tier institution.Extensive experience in high-performance computing, algorithmic trading, or large-scale AI/ML systems.Expertise in low-level programming, high-speed networking, and distributed architectures.A background in quantitative trading, AI research labs, or elite engineering teams where problem-solving is at the core of the role.A deep understanding of probability theory, convex optimization, and computational mathematics.Demonstrated ability to build and optimize mission-critical systems that operate under extreme constraints.Why Join?Work alongside world-class engineers, AI researchers, and quantitative traders in an intellectually stimulating, high-impact environment.Access to unlimited compute power, real-time market data at scale, and proprietary infrastructure built for speed and efficiency.A compensation structure that significantly exceeds the technology industry, with a direct link to performance and impact.A culture of intellectual freedom, rapid iteration, and minimal bureaucracy, where engineers have the autonomy to push the limits of what is possible.The opportunity to work on some of the most complex and high-stakes computing challenges in existence.How to ApplyFor highly qualified candidates with a background in elite computational engineering, this opportunity represents a rare chance to work at the forefront of systematic trading.To discuss this role in confidence, please contact Thalia Spolander at thalia.spolander@

Created: 2025-02-19