Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems.

Our trading teams are each comprised of a dynamic group of traders, quantitative researchers, and engineers who work together to examine the global markets, seeking to understand the complexities of various traded products and exchanges. They leverage their impeccable statistical analysis and data mining skills, using the results of their research to make forecasts and develop profitable predictive trading models.

You would join a team of technologists working directly alongside quantitative researchers and traders, building the platforms, tooling, and infrastructure that Jump's trading efforts run on. Sitting at the intersection of research infrastructure, systems engineering, and quantitative development, these teams build the software researchers and traders use to develop, test, scale, and run strategies. Depending on your background and interests, that could mean working on the firm-wide research platform or embedding directly within a trading team — in both cases the engineering problems are the same: performance, scale, and turning research ideas into dependable production software. This is a strong fit for an engineer who wants to work close to quantitative research and trading without being limited to a narrow support role. The work is broad and high impact: building core platform capabilities, improving performance and scalability, and partnering with researchers to turn mathematically informed ideas into robust production tooling.

What You'll Do:

• 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

Skills You’ll Need:

• 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
• Bonus skills:
  • 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

Benefits include:

• Private Medical, Vision and Dental Insurance
• Travel Medical Insurance
• Group Pension Scheme
• Group Life Assurance and Income Protection Schemes
• Paid Parental Leave
• Parking and Commuter Benefits