Global Markets, ML Engineer, Futures Strat, New York, NY / London

Global Markets, ML Engineer, Futures Strat

Futures Strats are responsible for all aspects of the futures electronic trading business, providing sophisticated execution-related services to the firm's clients, with a particular focus on automated execution algorithms. They are responsible for research, design, implementation, testing and support of high-performance algorithmic trading systems and strategies for the firm's futures trading businesses. The team interfaces on a regular basis with clients, sales-trading, technology, and other Strats teams.

Responsibilities:

• Design, build and maintain complex, scalable, low latency and high-capacity quantitative models for real time algorithmic trading, order state management, risk management, and other execution functions.
• Design and implement novel Reinforcement Learning and Machine Learning algorithms and approaches to provide generalizable solutions to complex, high-dimensional problems, ensuring efficiency and scalability across different markets.
• Develop and implement off-policy feedback mechanisms to continuously improve the accuracy and effectiveness of the Reinforcement Learning and Machine Learning models.
• Stay up to date with the latest advancements in Reinforcement Learning. Implement best practices to enhance the research infrastructure.
• Communicate complex technical concepts and findings to non-technical stakeholders in a clear and concise manner.
• Collaborate with cross-functional teams to understand business requirements and translate them into actionable Machine Learning solutions.

• A master's degree in Computer Science, Operations Research, Math, Physics or Statistics. A Ph.D. with a focus in Reinforcement Learning or a relevant area with a publication history is ideal.
• Demonstrated expertise in building Reinforcement Learning models for complex real-world problems and building robust training pipelines.
• Extensive experience implementing Reinforcement Learning or Machine Learning to achieve optimal control of dynamical systems/games, especially those with numerous opponents, asymmetric information, and states that evolve continually and rapidly.
• Proficiency in programming languages like Python, Java or C++ and the ability to write efficient, clean, and maintainable code.
• Excellent written and verbal communication skills, with the ability to convey technical concepts to both technical and non-technical audiences.