Agentic Systems Engineer for AI Agents & Automation

Tradermath; New York (NY); Hybride; USD 120 000 - 180 000; Plein temps

Avantages offerts par ce poste

Generous paid time off

Hybrid working opportunities

Free meals daily

Wellness reimbursement

Sports & fitness events

Volunteer opportunities

Résumé du poste

Tower Research Capital is a leading quantitative trading firm focused on building a high-performance platform and autonomous systems. We empower teams to innovate while maintaining scale and reliability across a global organization.

Our engineering teams tackle low-latency infrastructure, memory management for LLMs, and multi-agent orchestration, collaborating with researchers to automate and improve trading workflows. Hybrid work options are available.

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.

Responsabilités

• Architect internal agentic harness with the latest SOTA and memory handling improvements.
• Enable autonomous coding tasks and model building for collaboration.
• Integrate with MCP tools and develop integrations for Tower systems.
• Partner with quantitative developers and researchers to identify automation opportunities.
• Ensure system compliance with regulatory changes and isolation from production.

Connaissances

Software engineering experience

ML engineering

Distributed systems

Problem solving

Collaboration

Formation

Bachelor's or Master's degree in Computer Science, Engineering, Applied Mathematics

Outils

Open-source agentic frameworks (openclaw, hermes, langchain/langraph, opencode)

Description du poste

Tower Research Capital is a leading quantitative trading firm focused on building a high-performance platform and autonomous systems. We empower teams to innovate while maintaining scale and reliability across a global organization.

Our engineering teams tackle low-latency infrastructure, memory management for LLMs, and multi-agent orchestration, collaborating with researchers to automate and improve trading workflows. Hybrid work options are available.