Balyasny Asset Management is seeking exceptional Quantitative Developer Interns to join our Systematic Business.
Our investment process relies on robust, efficiently designed software to support research, investment decision-making, and trading.
You will have the opportunity to contribute to meaningful projects at the intersection of quantitative finance and technology. Potential workstreams may include systems design, data-processing, optimization, machine learning, analytics, and research infrastructure.
This is an opportunity to gain hands-on experience solving cutting-edge quantitative engineering challenges at a leading multi-strategy investment firm.
RESPONSIBILITIES
- Design and build scalable software components that support systematic investment workflows.
- Develop and improve data-processing, analytics, optimization, and machine-learning tools used by the Systematic Business.
- Work with Kubernetes-based deployments and contribute to more efficient, reliable development and production processes.
- Enhance real-time monitoring, alerting, and metrics to improve visibility into data pipelines and system health.
- Collaborate with quantitative researchers, quantitative developers, and business stakeholders to translate investment and operational requirements into practical technical solutions.
- Communicate progress, technical decisions, and results clearly to team members and stakeholders.
QUALIFICATIONS & REQUIREMENTS
- Bachelor’s and Master's student graduating between Winter 2027 and Spring/Summer 2028 who are pursuing a degree in Computer Science, Software Engineering, Computer Engineering, or a related STEM discipline, with strong programming experience.
- Strong proficiency in Python and/or C++; experience writing clean, efficient, and maintainable code is preferred.
- Interest in quantitative finance, systematic investing, market-data systems, or research and trading infrastructure.
- Familiarity with data engineering, distributed systems, cloud computing, Kubernetes, optimization, or machine learning is a plus.
- Self-starter with a results-oriented mindset, strong intellectual curiosity, and a desire to learn quickly.
- Excellent attention to detail, problem-solving ability, and communication skills.