Senior Machine Learning Engineer (GPU Optimization)

Fintal Partners (New York NY, USA) Follow 2 days ago

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.

Key Responsibilities

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.

Requirements

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.

Apply Now

Save Job