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.

Do not pass up this chance, apply quickly if your experience and skills match what is in the following description.

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, xhqgsiq 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.