Jobs at WorldQuant

The AI Software Developer will collaborate with teams to create AI-driven applications, manage product deliverables, and stay updated on AI technologies while solving complex, real-world problems.

WorldQuant seeks candidates for its Technology team to join a community for potential future opportunities. The role highlights collaboration and innovative thinking.

The role involves writing low latency C++ code, implementing trading infrastructure, and adapting systems for the next generation trading platform, requiring collaboration with portfolio managers.

Support Portfolio Managers with alpha research, modeling, and implementation of quantitative trading strategies while building tools for quantitative research.

Develop systematic strategies to exploit market inefficiencies, manage a quantitative investment portfolio, and build a research pipeline.

Develop and manage systematic strategies for a diverse range of asset classes, utilizing quantitative management techniques to drive investment portfolios.

Lead the maturation phase of crucial software systems, improving performance and scalability, while mentoring a team of developers.

Lead a high-performing team to manage electronic execution, focusing on recruitment, development, and collaboration while adapting best engineering practices.

Design, implement, test and deploy high-performance, large-scale software systems to support quantitative research and production portfolios. Own Linux/C++ codebases, apply system programming, concurrency, and data structures expertise, and collaborate with researchers to build scalable distributed and streaming data platforms.

Manage a live trading book while utilizing cutting-edge agentic AI solutions. Responsible for risk management, strategy development, and model building within a quantitative trading environment.

The role involves leading the development and maintenance of execution algorithms and frameworks, optimizing trading efficiency across cash and derivative products.

Conduct ML research to produce predictive trading signals, design deep learning architectures, apply LLM-based agentic methods, implement signal compression and combination, build model frameworks for investment use, and collaborate with portfolio managers and researchers.