Machine Learning Performance Engineer

A leading quantitative trading firm is seeking a Machine Learning Performance Engineer to join a small, highly technical team leading deep learning trading strategy in live markets. The strategy is early in its development, so this is a chance to build core performance infrastructure alongside the lead researcher instead of inheriting a finished platform. You'll work at the intersection of deep learning, GPU programming, and systems engineering. Speed and efficiency of both training and inference translate directly into trading performance. What You’ll Work On Write (from scratch) and optimize custom CUDA kernels for latency- and throughput-critical deep learning workloads Build scalable, robust training and inference pipelines Identify and eliminate performance bottlenecks across the GPU, memory hierarchy, and software stack Dig into the internals of open-source deep learning frameworks (PyTorch, JAX) and extend them where needed Work directly with researchers and traders to turn model ideas into fast production systems Develop a deep understanding of the trading systems your work supports Qualifications Hands-on CUDA kernel development, with strong knowledge of Tensor Cores, cooperative groups, CUDA Graphs, and warp-level intrinsics Strong C++ and Python Deep understanding of computer architecture, including memory hierarchy and GPU execution Expertise in the internals of deep learning frameworks such as PyTorch, JAX, or TensorFlow Experience building or optimizing high-performance deep learning systems in any domain (robotics, recommendations, audio, video, biology, and so on) Nice to Have Experience with Triton, CUB, cuDNN, or cuBLAS JAX ecosystem experience (XLA, Flax) Linux systems programming Large-scale distributed training Open-source contributions to ML or GPU projects Prior finance or trading experience is not required.