Systems Software Engineer; AI inference stack, CUDA)

Relocation Level: TBD

• Design and modify AI inference software stacks to enable proof-of-concept hardware capabilities.
• Develop software to quantify and demonstrate the value of proposed system features.
• Lead software architecture and development efforts, both independently and as a technical lead for small teams.
• Research emerging AI software topics to identify memory and storage system challenges and opportunities.
• Develop and modify Linux applications and libraries using C, C++, Python, Rust, CUDA, and/or SYCL for advanced heterogeneous computing systems.
• Develop and use emulators to predict system‑level performance and functional impacts in advance of hardware availability.
• Debug system‑level issues spanning applications, libraries, operating systems, kernels, system configuration, networks, and hardware/software interfaces.
• Use emerging AI tools to improve productivity.
• Collaborate across Micron organizations and with external partners.
• Mentor interns and earlier-career engineers.
• Present technical findings, insights, and innovations to internal or external customers.

• Bachelor’s or Master’s degree with 10+ years of experience, or a PhD with 7+ years of experience, in Computer Science, Software Engineering, Computer Engineering, or a related field.
• Working knowledge of end-to-end AI inference software stack architectures, with hands‑on development experience in multiple layers, such as frameworks, inference engines, ML compilers, compiler infrastructure, hardware runtimes, or hardware abstraction layers.
• Experience debugging issues spanning applications, libraries, operating systems, kernels, networks, and hardware/software interfaces.
• Proficient development skills in C, C++ and Python, with Linux‑specific programming experience such as multithreading, IPC, ioctl, GDB, and the /proc and /sys file systems.
• Experience programming GPUs or other accelerators using CUDA, SYCL, OpenCL, or a comparable programming model.
• Demonstrated ability to lead technical initiatives without supervisory authority.
• Familiarity with LLM serving internals, including KV‑cache management, prefill and decode phases, KV‑cache offloading, and disaggregated serving.
• Familiarity with inference engines and distributed serving frameworks such as SGLang, vLLM, NVIDIA Dynamo, or TensorRT‑LLM.
• Strong understanding of heterogeneous compute architecture concepts, including memory hierarchies, NUMA, caching, accelerator architectures, and interconnects such as PCIe, CXL, NVLink, or UALink.

• Technical leadership experience in a systems-focused pathfinding or research organization.
• Technical knowledge of HBM, DRAM, and emerging memory technologies.
• Technical knowledge of NVMe SSDs, NAND, and next‑generation storage systems.

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• Additiona…