Machine Learning Research Engineer

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. Summary: As an AI/ML Applied Research Engineer, you will sit at the cutting-edge intersection of our central machine learning infrastructure and our research teams. Your core mandate is to act as "Customer Zero" for our internal ML Research platform. You will focus on expanding our ML research platform to benchmark, rapidly prototype, and stress-test both software and hardware layers across our entire distributed ML stack. By leveraging AI agents and auto-research capabilities, you will push our systems to their limits, identify bottlenecks, and create a frictionless environment to test novel machine learning models on realistic, large-scale data. Ultimately, by hands-on testing these systems yourself, you will act as a technical advisor. You will share insights on research progress, evaluate how new ideas fare in practice, and help guide the strategic direction of our central engineering efforts. 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. Qualifications: Strong Software Engineering Foundation: Deep proficiency in Python and software d…