Machine Learning Research Engineer

Tower Research Capital is a quantitative trading firm that develops high-performance trading and engineering systems. The AI/ML Applied Research Engineer will validate and benchmark the firm’s distributed machine learning infrastructure, streamline research prototyping, and use AI agents to stress-test systems. The role will also connect infrastructure engineering with machine learning research by documenting findings and advising teams on platform improvements and effective use of the technology stack.

Responsibilities:

• Platform Validation & Infrastructure Benchmarking:
• Serve as the primary feedback loop for the entire ML stack
• Actively run complex models through our full ML pipeline to comprehensively test both the training and inference environments
• Validate the central infrastructure in practice, seeing exactly how new research ideas fare and identifying system bottlenecks before broader rollout to research teams
• Streamline Rapid Prototyping for ML Research:
• Build high-level abstractions that allow users to bypass setup friction
• Integrate our core ML tooling directly with our underlying simulation and data frameworks, providing a unified entry point to access our full tech stack
• Enable rapid iteration on real-world data and seamless distributed training via Ray
• Agentic Workflows for ML Research:
• Leverage AI agents and auto-research workflows to autonomously generate experiments, stress-test our distributed clusters, and provide data-driven, actionable feedback on what infrastructure needs to be optimized or built next
• Research Platform Feedback & Insights Sharing:
• Act as the critical bridge between infrastructure builders and ML researchers
• Be the first to exhaustively test new models and push the platform's limits
• Document and publish empirical findings on system capabilities and hardware performance
• Take your validated insights to assist engineering teams with platform improvements and advise researchers on how to best leverage the stack