Agentic System Developer

Tower Research Capital is a leading quantitative trading firm founded in 1998. Tower has built its business on a high-performance platform and independent trading teams. We have a 25+ year track record of innovation and a reputation for discovering unique market opportunities.

Tower is home to some of the world’s best systematic trading and engineering talent. We empower portfolio managers to build their teams and strategies independently while providing the economies of scale that come from a large, global organization. 

Engineers thrive at Tower while developing electronic trading infrastructure at a world class level. Our engineers solve challenging problems in the realms of low-latency programming, FPGA technology, hardware acceleration and machine learning. Our ongoing investment in top engineering talent and technology ensures our platform remains unmatched in terms of functionality, scalability and performance.

At Tower, every employee plays a role in our success. Our Business Support teams are essential to building and maintaining the platform that powers everything we do — combining market access, data, compute, and research infrastructure with risk management, compliance, and a full suite of business services. Our Business Support teams enable our trading and engineering teams to perform at their best.

At Tower, employees will find a stimulating, results-oriented environment where highly intelligent and motivated colleagues inspire each other to reach their greatest potential.

Responsibilities

• Architecting and improving our internal agentic harness according to the latest SOTA, enhanced memory handling, subagentic distribution, supporting the latest LLMs
• Implementing long-horizon support to enable autonomous coding tasks, performance improvements, and model building, ensuring the system functions effectively as a collaborative team member
• Integrating with and developing tools and MCP integrations for various systems at Tower
• Partnering with quantitative developers and researchers to identify and prioritize automation opportunities 
• Making sure the system is compliant with the changing regulatory landscape and isolating the system from production

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Applied Mathematics, or a related field
• 2–5 years of professional experience in software engineering, data science, or ML engineering
• Experience with open-source agentic frameworks (e.g., openclaw, hermes, langchain/langraph, opencode) and building custom agentic systems
• Deep understanding of distributed systems, memory maintenance for LLMs, and multi-agent orchestration