Senior AI Software Development Engineer

Intel Corporation•Santa Clara, CA

8d•$149,750 - $275,580•Hybrid

About The Position

Intel is seeking a senior AI systems software engineer to lead optimization strategy and technical development across the AI software stack. The role focuses on high-performance software solutions for AI inference workloads—including Large Language Models, Generative AI, and emerging machine learning applications—on Intel GPU platforms. The engineer will collaborate with teams working on hardware architecture, compilers, runtimes, and AI frameworks to deliver AI performance improvements and help influence future product direction. The position involves leading optimization initiatives, defining strategies across compilers, runtimes, and GPU kernels, and architecting software that improves scalability, efficiency, and performance. The engineer will profile and benchmark complex workloads, analyze system bottlenecks, and use performance characterization to inform hardware and software co-design decisions. The role also includes driving cross-functional technical initiatives, mentoring engineers, and evaluating emerging AI technologies and optimization opportunities. The role is based in the United States, with primary locations in Folsom, California, and additional locations in Santa Clara, California, and Hillsboro, Oregon. It is eligible for Intel’s hybrid work model, allowing employees to split time between an assigned Intel site and off-site work. The position is an experienced hire role on Shift 1.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field and 5+ years of experience; or a master's degree in the same field and 3+ years of experience; or a PhD in the same field and 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.
Demonstrated technical judgment and ability to solve complex, high-impact challenges.
Ability to think strategically across software and hardware systems and identify performance, scalability, and efficiency opportunities.
Ability to lead through influence and build alignment across cross-functional organizations.
Effective communication with technical and business audiences.
Strong ownership and accountability, driving initiatives from strategy through execution.
Experience using data, experimentation, and measurable results to guide decisions.

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, or ONNX Runtime.
Experience with graph compilation and optimization technologies such as MLIR, LLVM, TVM, or XLA.
Experience with GPU programming models such as Vulkan Compute, OpenCL, SYCL, CUDA, HIP, or equivalent technologies.
Knowledge of neural network optimization techniques, including quantization, kernel fusion, memory optimization, and performance tradeoffs.
Experience across multiple AI software stack layers, including inference runtimes and serving frameworks, graph compilers and optimization frameworks, and GPU kernels or performance-critical compute code.
Contributions 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 across architecture, compiler, framework, and runtime teams.
Mentor engineers and provide technical leadership across projects.
Evaluate emerging AI technologies and identify future optimization opportunities.
Use data, experimentation, and measurable results to inform decisions and technical direction.
Communicate complex concepts effectively to technical and business audiences.