Quantitative Developer

WizardQuant; New York (NY); On-site; USD 150,000 - 190,000; Full time

Job summary

WizardQuant in New York develops quantitative research platforms and production trading systems. Developers collaborate with researchers and portfolio management to build core components, backtesting, and data ingestion tools for end-to-end model execution and portfolio management.

The role emphasizes research and production pipelines, performance optimization, and scalable computing across global markets. Strong C++ or Python skills and Linux development are essential, with 3+ years of

Qualifications

• Bachelor's degree or above in CS, software engineering, mathematics or related STEM field.
• Strong foundation in algorithms and data structures, and Linux development experience.
• 3+ years in quantitative software engineering, with design and development of research/production systems.
• Hands-on experience with modern C++ or Python, including concurrent/parallel programming.

Responsibilities

• Develop quantitative research platforms and tools, such as simulation and backtesting systems.
• Participate in algorithm research and optimization for workflows like portfolio optimization.
• Build, optimize, and maintain low-latency trading pipelines and order execution systems.
• Utilize high-performance computing frameworks to optimize trading components.
• Contribute to emerging business areas across global markets.

Skills

Algorithms and data structures

Linux proficiency

Concurrency and parallel programming

Quantitative software engineering

Project management

Education

Bachelor's degree or above in Computer Science, Software Engineering, Mathematics or related STEM fields

Tools

C++

Python

GPU computing

PyTorch

Job description

Quantitative Developers work closely with Quantitative Researchers, Portfolio Management and other teams to develop, evolve, and maintain core components and systems within the quantitative research and production life cycles. They develop components such as optimizers; tools and computing frameworks like simulation and backtesting platforms; and systems including market data ingestion, trading execution, and order management. They are responsible for the end-to-end implementation and support of model execution, portfolio construction and management based on mathematical and computer science methodologies.

Responsibilities

• Develop quantitative research platforms and tools, such as simulation and backtesting systems, to support quantitative research computations in both interactive and batch-processing modes.
• Participate in algorithm research and implementation optimization for workflows like portfolio optimization, based on a deep understanding of business logic and engineering expertise.
• Build, optimize, and maintain low-latency trading pipelines and order execution systems, particularly within the context of global markets.
• Utilize or develop high-performance and parallel computing frameworks or libraries to optimize the performance of trading components in both research and production environments.
• Contribute to emerging business areas, including new investment horizons, trading frequencies, asset classes, and global markets.

Qualifications

• Bachelor’s degree or above, majoring in Computer Science, Software Engineering, Mathematics, or related STEM fields.
• Solid foundation in algorithms, data structures, and proficiency in software development within Linux environments.
• 3+ years of experience in quantitative software engineering, with proven experience in designing and developing research and production quantitative systems. Strong project management skills.
• Hands-on professional experience with modern C++ or Python, including concurrent and parallel programming.

Preferred Qualifications

• Experience in U.S. futures and equities markets and relevant quantitative trading systems.
• Deep understanding of low-latency, production trading software systems.
• Experience with quantitative research systems, linear algebra, numerical computations, and numerical optimization.
• Experience applying high-performance computing techniques to process large-scale time-series financial datasets.
• Experience with GPU computing and distributed computing.
• Solid understanding of computer science fundamentals such as architecture, OS and compilers.
• Experience with machine learning frameworks such as PyTorch.