> Markdown version of [/jobs/ext/3968021-senior-deep-learning-compiler-engineer](https://www.wearedevelopers.com/jobs/ext/3968021-senior-deep-learning-compiler-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Deep Learning Compiler Engineer - **Company:** NVIDIA Corporation - **Location:** United States - **Experience:** Expert - **Salary:** $152,000.0 - $241,500.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, C++ (Programming Language), Nvidia CUDA, Computer Engineering, Software Debugging, Python (Programming Language), OpenCL, Software Engineering, Graphics Processing Unit (GPU), Computer Network Operations, Pytorch, Deep Learning, LLVM, Information Technology, MLIR (Multi-Level Intermediate Representation) - **Published:** October 27, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-deep-learning-compiler-engineer-wa--f4ae37ca-94ac-4a7f-bd2d-cbe4787dd03a ## About the Role Deep Learning, Autonomous Driving Systems, GPU (Graphics Processing Unit), Artificial Intelligence (AI), Inference Engine, Robotics, Memory Hardware, Network Operations Center, Deep Learning, Team Building, * Bachelors, Masters or Ph.D. in Computer Science, Computer Engineering, related field or equivalent experience * 3+ years of relevant work or research experience in performance analysis and compiler optimizations. * Ability to work independently, define project goals and scope, and lead your own development efforts. * Excellent C/C++ and Python programming and software design skills, including debugging, performance analysis, and test design. * Strong interpersonal skills are required along with the ability to work in a dynamic product-oriented team. Ways to stand out from the crowd: * Proficient in CPU and/or GPU architecture. CUDA or OpenCL programming experience. * Experiences in systems with constrained resources, such as embedded platforms, small memory size, and cross compilation. * Experience with the following technologies: MLIR, XLA, TVM, LLVM, deep learning models and algorithms, and deep learning frameworks, such as PyTorch. * GPU kernel generation with high performance and fast build time. * A track record of success in mentoring junior engineers and interns is a bonus.