Quantitative Developer | Trading Team

About the Role

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.

We are seeking a Quantitative Developer to help support a New York based trading team. In this role, you will collaborate closely with the team's researchers to maintain the team's infrastructure and production systems. This is a hybrid development and operations position. You'll be responsible for ensuring the reliability of the team's production pipeline—diagnosing and fixing recurring issues from a systems perspective, making jobs more stable, and stepping in when things go wrong. We are looking for a mature, self-directed engineer who can operate independently and handle high-stakes situations with confidence. The more efficiently you can automate and stabilize operations, the more time you'll have to focus on performance-oriented development work.

What You'll Do

• Own live production operations: Ensure trading strategies are connected to all markets, data is available in the correct volume and format, and the optimizer is running as scheduled.

• Manage and evolve the monitoring system: Act as the primary escalation point for error messages and failed jobs. Chase problems identified by the monitoring system to their source—identifying bugs, writing fixes, and resolving issues yourself rather than forwarding them on. Maintain a big-picture understanding of all processes overseen by the monitoring system.

• Improve pipeline efficiency and performance: Investigate recurring pipeline issues and solve them at a systemic level. Optimize performance-critical workloads—for example, making ML models and GPU jobs run faster and more efficiently.