Senior AI Software Development Engineer

Intel•Folsom, CA

6d•Hybrid

About The Position

Intel is seeking a Senior AI Systems Software Engineer to drive optimization strategy and technical leadership across the AI software stack. The role focuses on developing high-performance software solutions that accelerate AI inference workloads—including Large Language Models, Generative AI, and emerging machine learning applications—on Intel GPU platforms. The engineer will collaborate with software, compiler, runtime, framework, and hardware architecture teams to improve AI performance and influence future product direction. The position involves leading optimization initiatives, developing performance strategies across compilers, runtimes, and GPU kernels, and architecting software for greater scalability, efficiency, and performance. It also includes profiling and analyzing complex workloads, informing hardware and software co-design through performance characterization, leading cross-functional initiatives, mentoring engineers, and evaluating emerging AI technologies. The role is based in Folsom, California, with additional locations in Santa Clara, California, and Hillsboro, Oregon. It is eligible for Intel’s hybrid work model, which allows employees to split their time between an assigned Intel site and off-site work. The annual US salary range is $149,750–$275,580, with pay determined by location and other factors. Intel describes its compensation package as including competitive pay, stock bonuses, and health, retirement, and vacation benefits.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field with 5+ years of experience; or a master's degree in the same field with 3+ years of experience; or a PhD in the same field with 1+ years of experience.
Experience in software development, AI systems, or performance engineering.
C++ software development and debugging experience.
Experience with performance analysis, profiling, and optimization of compute-intensive applications.
Experience with modern neural network architectures and their execution on hardware accelerators.

Nice To Haves

Experience with GPU architectures, AI accelerators, or parallel computing systems.
Experience optimizing end-to-end AI inference workloads in production environments.
Familiarity with AI inference frameworks such as llama.cpp, vLLM, OpenVINO, PyTorch, TensorRT, ONNX Runtime, or similar technologies.
Experience with graph compilation and optimization technologies such as MLIR, LLVM, TVM, XLA, or related compiler frameworks.
Experience with GPU programming models such as Vulkan Compute, OpenCL, SYCL, CUDA, HIP, or equivalent technologies.
Knowledge of neural network optimization techniques, quantization, kernel fusion, memory optimization, and performance tradeoffs.
Experience across multiple layers of the AI software stack, including AI inference runtimes and serving frameworks, graph compilers and optimization frameworks, and GPU kernels and performance-critical compute code.
Experience contributing to open-source software projects.

Responsibilities

Lead optimization initiatives for AI inference workloads on Intel GPUs.
Define and drive performance optimization strategies across compilers, runtimes, and GPU kernels.
Architect software solutions that improve scalability, efficiency, and performance.
Profile, benchmark, and analyze complex AI workloads to identify system bottlenecks.
Influence hardware and software co-design decisions through performance characterization and workload analysis.
Drive cross-functional technical initiatives spanning architecture, compiler, framework, and runtime teams.
Mentor engineers and provide technical leadership across projects.
Evaluate emerging AI technologies and identify future optimization opportunities.