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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # GPU Performance Engineer - **Company:** Susquehanna International Group, LLP - **Location:** United States - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Artificial Neural Networks, C++ (Programming Language), Nvidia CUDA, Scientific Computating, Graphics Processing Unit (GPU), Information Technology, ONNX (Open Neural Network Exchange) Format, Xgboost, TensorRT, Software Coding - **Published:** July 23, 2026 - **Apply:** https://www.dice.com/job-detail/bdc060be-e8db-480f-80b8-26c97011af3d ## About the Role * Strong proficiency in writing and optimizing CUDA kernels * Solid programming experience in C/C++ (preferred) * Deep understanding of GPU architecture, including memory hierarchy, SIMT execution, occupancy, and latency/throughput tradeoffs * Ability to reason about numerical stability, precision, performance tradeoffs, and how model design choices affect hardware efficiency * Strong problem-solving skills and comfort working with low-level systems Preferred qualifications * PhD in mathematics, physics, computer science, engineering, or related quantitative field * Strong background in linear algebra, probability, numerical methods, or scientific computing * Experience working with quantitative research teams or financial models * Demonstrated ability to improve real-world inference performance beyond baseline framework or library implementations * Familiarity with PTX-level behavior, tensor core utilization, or architecture-specific tuning * Exposure to ONNX Runtime, TensorRT, Triton, TVM, or similar systems * Exposure to neural networks, tree-based models (e.g., LightGBM), state space models (e.g., Mamba architectures), and experience with kernel fusion, custom operators, model compilation, or graph-level optimization ## Description We are looking for a GPU Performance Engineer to build highly optimized CUDA kernels for low-latency inference. This role is focused on workloads where off-the-shelf runtimes and vendor libraries do not fully exploit the structure of the model, and where custom kernels, memory layouts, and execution strategies can deliver meaningful gains. You will work closely with quantitative researchers and engineers to understand model structure, identify computational bottlenecks, and turn mathematical ideas into production-grade GPU implementations. You will use your understanding of GPU hardware to help shape models that are both mathematically effective and efficient to run. The problems span compact neural networks, tree-based models, and other structured inference workloads where latency, throughput, and efficiency all matter. This role is a strong fit for someone who enjoys low-level optimization, performance analysis, and translating abstract models into hardware-efficient code. What you'll do * Design, implement, and optimize custom CUDA kernels for latency-critical inference workloads * Develop fine-grained GPU implementations tailored to specific model structures * Analyze quantitative research models and computational bottlenecks to identify opportunities for parallelization and hardware-efficient execution * Collaborate directly with quantitative researchers to translate mathematical models into high-performance compute pipelines * Optimize end-to-end inference performance through kernel tuning, memory-layout design, execution strategy, I/O optimization, and precision tradeoffs * Profile and benchmark GPU performance * Improve latency and throughput in production inference systems * Contribute to GPU architecture decisions and performance best practices ## Related Videos - [Coffee with Developers - Stephen Jones - NVIDIA](https://www.wearedevelopers.com/videos/1303-coffee-with-developers-stephen-jones-nvidia) - [Accelerating Python on GPUs](https://www.wearedevelopers.com/videos/859-accelerating-python-on-gpus) - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [TikTok's Privacy Innovation](https://www.wearedevelopers.com/videos/1036-tiktok-s-privacy-innovation) - [Trends, Challenges and Best Practices for AI at the Edge](https://www.wearedevelopers.com/videos/630-trends-challenges-and-best-practices-for-ai-at-the-edge) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) ## Related Articles - [What’s the latest in NVIDIA CUDA Python](https://www.wearedevelopers.com/magazine/568-what-s-the-latest-in-nvidia-cuda-python) - [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence) - [Dev Digest 157: CUDA in Python, Gemini Code Assist and Back-dooring LLMs](https://www.wearedevelopers.com/magazine/557-dev-digest-157-cuda-in-python-gemini-code-assist-and-back-dooring-llms) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [7 Cloud Computing Trends Coming in 2025 for Developers](https://www.wearedevelopers.com/magazine/412-7-cloud-computing-trends-coming-in-2025-for-developers)