Requirements:

At least 5+ years of professional software engineering experience in Python
Experience designing, analyzing, and implementing highly algorithmic code
Experience building production systems in Linux environments
Strong understanding of data structures, algorithmic complexity, and efficient implementation
Experience with concurrent and distributed systems
Familiarity with cluster, cloud, or other large-scale compute environments
Self-directed, intellectually curious, and comfortable taking ownership of meaningful technical problems
Minimum academic qualification: Bachelor's degree in Computer Science, Computer Engineering, Mathematics, Physics, or equivalent
Background in research infrastructure, high-performance computing, distributed systems, or performance engineering
Experience improving bottlenecks, scalability, or migration paths from researcher-friendly tooling into higher-performance systems
Ability to communicate technical tradeoffs clearly with both engineers and quantitative users

What You'll Be Doing:

Design, build, and improve the research and production infrastructure that underpins Jump's trading efforts
Work across a hybrid Python/C++ environment, balancing researcher usability with performance-critical systems development
Partner closely with quantitative researchers and traders to translate research workflows, models, and ideas into scalable, maintainable software
Develop foundational libraries, backend systems, and workflow tooling used to support strategy research, testing, and deployment
Improve system performance, algorithmic efficiency, and scalability across distributed and cluster-based compute environments
Own projects end to end, from design and implementation through testing, rollout, and ongoing improvement
Contribute to the evolution of the platform as the team expands its research capabilities and compute infrastructure

Experience with Python/C++ interoperability and library development
Ability to work effectively with quantitative researchers and translate mathematical or theoretical ideas into practical software
Exposure to time series analysis, optimization, simulation, numerical methods, or other quantitative workflows