AI Infrastructure Engineer | Systematic trading

Senior AI Infrastructure Engineer | Systematic Trading | New York The buy side is entering one of the most significant technology shifts it has experienced in decades. AI is changing how investment research is conducted, how signals are discovered, and how firms compete. The most sophisticated systematic trading firms are beginning to rebuild their research platforms around that future - we're partnered with one of them. The fastest feedback loop in AI isn't a benchmark. It's a P&L statement. The environment spans large-scale reinforcement learning, agentic research workflows, GPU-native simulation, advanced machine learning systems, and the training and inference infrastructure that supports them at scale. Your work has fast, real impact. Models that improve predictive accuracy show up in P&L. Infrastructure that compresses iteration cycles changes what researchers can attempt next quarter. Better simulation environments expand the range of ideas that can be explored and deployed. You'll build the systems and infrastructure that make all of this possible — partnering closely with researchers, shaping the technical agenda, and owning the platform that underpins the research lifecycle. The engineering challenges are serious: distributed GPU compute, ultra-high-performance inference, CUDA and Triton optimisation, RDMA networking, and performance-critical C++ and Rust systems, all designed to accelerate the full lifecycle from idea generation through live deployment. The team is looking for engineers who gravitate toward hard systems problems: scaling limits, performance bottlenecks, distributed compute, optimisation under real-world constraints, and infrastructure where efficiency directly impacts research capability and trading performance. You'll work alongside people from elite quant firms, frontier AI labs, advanced HPC environments, and deeply technical engineering teams. The culture is highly technical, low bureaucracy, and built around autonomy, speed, and first-principles thinking. Engineers work closely with researchers, influence core architectural decisions, and operate in an environment where technical quality matters more than hierarchy or process. Compensation is top of market, with meaningful upside tied to performance. Very few firms are genuinely positioned to help define what AI-native quantitative research will look like over the next decade. #J-18808-Ljbffr Kalpa Group