Intern - AI Systems and Infrastructure Engineering

Micron • Austin, TX

8d

About The Position

The Advanced Systems Research and Engineering team develops innovative hardware and software technologies that enable the next generation of Artificial Intelligence infrastructure. The team collaborates closely with engineering, architecture, product, and research organizations to evaluate emerging AI workloads and drive advancements in memory, storage, interconnects, and distributed computing platforms. Through systems research, prototyping, and performance analysis, the team helps shape future technology roadmaps and industry-leading solutions. The AI Systems Software Engineering Intern will work alongside senior engineers and researchers on advanced systems software for Large Language Models (LLMs) and Agentic AI applications. This role focuses on characterizing and improving the performance, scalability, and efficiency of AI inference and training workloads across GPU platforms and heterogeneous memory, interconnect, and storage systems. The intern will contribute to profiling, workload characterization, systems optimization, and experimental evaluation, helping drive innovations in AI infrastructure and memory technologies.

Requirements

• Currently pursuing a Master's or Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field.
• Demonstrated experience with AI systems, machine learning systems, computer systems research, or systems software development through coursework, research, or projects.
• Understanding of Large Language Models (LLMs), including transformer execution, attention mechanisms, KV cache behavior, batching, token-level latency, throughput, and memory performance considerations.
• Proficiency in Python and C/C++, with hands-on experience developing, debugging, and optimizing software in Linux environments.
• Experience using GPU-based performance analysis tools and at least one modern AI framework or serving stack, such as PyTorch, vLLM, TensorRT-LLM, NVIDIA Dynamo, or related technologies.

Nice To Haves

• Experience extending or optimizing LLM runtimes, serving engines, schedulers, or distributed inference frameworks.
• Hands-on experience implementing advanced KV-cache, memory management, or state-management techniques for long-context or stateful AI applications.
• Experience with GPU optimization technologies such as CUDA, Triton, NCCL, RDMA, or similar accelerator and communication frameworks.
• Familiarity with heterogeneous memory architectures, including HBM, DRAM, CXL-attached memory, NVMe storage, pooled memory, or disaggregated memory systems.
• Evidence of significant technical impact through publications, patents, open-source contributions, or substantial research and engineering projects related to Artificial Intelligence, distributed systems, memory systems, or high-performance computing.

Responsibilities

• Develop and enhance systems software, profiling tools, and experimentation frameworks for LLM training, LLM inference, and Agentic AI workloads.
• Design, implement, and evaluate memory- and state-management techniques, including caching, tiering, compression, eviction, and lifecycle management for AI serving environments.
• Characterize and optimize AI workload execution across GPUs, CPUs, memory subsystems, storage, and distributed infrastructure, with a focus on latency, throughput, scalability, and resource utilization.
• Build benchmarking, simulation, and automation capabilities to evaluate data placement, migration, scheduling, and performance behavior across heterogeneous memory systems.
• Collaborate with engineering and research teams to develop representative AI workloads, analyze experimental results, and contribute to technical publications, intellectual property, and future platform designs.

Benefits

• Choice of medical, dental and vision plans
• Benefit programs that help protect your income if you are unable to work due to illness or injury
• Paid family leave
• Robust paid time-off program
• Paid holidays

What This Job Offers

• Job Type: Full-time
• Career Level: Intern