Senior Machine Learning Engineer (GPU Optimization)

• Design, develop, and optimize ML models and inference pipelines for latency-sensitive trading applications.
• Build high-performance GPU-accelerated systems using CUDA and modern C++.
• Profile and optimize compute, memory, and networking bottlenecks across large-scale distributed environments.
• Collaborate closely with researchers, quant traders, and infrastructure engineers to deploy production-grade ML solutions.
• Drive performance improvements across GPU architectures, kernels, and training/inference workflows.

• Strong commercial experience in Machine Learning Engineering, Software Engineering, or High-Performance Computing environments.
• Expert-level C++ development skills and deep understanding of low-level systems programming.
• Extensive experience with CUDA, GPU programming, and performance optimization.
• Proven track record optimizing GPU utilization, memory management, kernel performance, and distributed compute workloads.
• Experience profiling and debugging performance-critical applications.
• Background in quantitative finance, trading, scientific computing, AI infrastructure, or other high-performance environments is highly desirable.
• Strong understanding of parallel computing, computer architecture, and modern ML frameworks.

Nice to Have

• Experience within HFT, quantitative trading, or low-latency systems.
• Familiarity with distributed training frameworks and large-scale ML infrastructure.
• Knowledge of FPGA, networking, or hardware acceleration technologies.

Our client is a leading quantitative trading firm seeking a Senior Machine Learning Engineer to build and optimize next-generation ML infrastructure powering ultra-low-latency trading systems. This role sits at the intersection of machine learning, high-performance computing, and systems engineering, with a strong focus on GPU acceleration and performance optimization.