AI Software Development Engineer

Intel•Folsom, CA

1d•Hybrid

About The Position

Intel is seeking an AI Software Development Engineer to optimize AI inference workloads for Intel GPUs. The role works across the AI software stack, focusing on performance-critical GPU kernels, graph compilation flows, and inference runtimes. Contributions will improve the efficiency and scalability of modern AI workloads and Intel AI technologies. The position is part of Intel’s Software Group, which develops software solutions, tools, frameworks, and optimizations for Intel AI products. The role collaborates with hardware, driver, compiler, and framework teams to advance hardware/software co-optimization and contributes to open-source AI inference ecosystems.

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field, with 4 or more years of relevant industry experience; or a master’s degree with 3 or more years of experience; or a Ph.D. with no prior professional experience.
Strong proficiency in C++ programming, debugging, and problem-solving.
Solid understanding of GPU architectures, graphics device drivers, firmware, AI accelerators, or parallel computing systems.
Experience with performance analysis, profiling, and optimization for compute-intensive applications.
Hands-on experience with modern neural network architectures and their execution on hardware accelerators.

Nice To Haves

Experience optimizing end-to-end AI inference workloads in production environments.
Familiarity with AI inference frameworks such as OpenVINO, PyTorch, TensorRT, ONNX Runtime, or similar technologies.
Knowledge of neural network optimization techniques, including quantization, kernel fusion, and memory optimization.
Experience across multiple layers of the AI software stack, including inference runtimes, graph compilers, and GPU kernels.
Experience contributing to open-source software projects.

Responsibilities

Optimize AI inference workloads, including large language models, multimodal models, and generative AI applications, for Intel GPUs.
Design, develop, and tune performance-critical GPU kernels and runtime components using C++, SYCL, Vulkan Compute, OpenCL, or similar parallel programming technologies.
Develop and optimize graph compilation flows and compiler passes using MLIR, LLVM, or related frameworks.
Identify and resolve performance bottlenecks across compiler, runtime, framework, and kernel layers.
Profile, benchmark, and analyze AI workloads to validate performance improvements and guide optimization strategies.
Collaborate with hardware, driver, compiler, and framework teams to drive hardware/software co-optimization.
Contribute to and optimize open-source AI inference frameworks such as OpenVINO, llama.cpp, vLLM, PyTorch, or related ecosystems.