Member of Technical Staff, AI Engineering

Our vision is to transform how the world uses information to enrich life for all.

Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever.

The Smart Manufacturing and AI team builds advanced machine learning, GenAI, and Agentic AI solutions that directly power Micron's manufacturing advantage. We work at the intersection of cutting‑edge AI and real‑world production systems, turning massive data and compute into measurable impact. If you enjoy solving hard problems at scale and seeing your work drive silicon to market, this is the team for you!

This role is critical to how we train, optimize, and deploy large‑scale AI systems on modern GPU platforms. As a GPU Performance Engineer, you will push the limits of multi‑GPU and distributed training, shape next‑generation AI workloads, and partner closely with data scientists, engineers, and hardware architects. Your work will directly influence performance, cost, and speed across Micron's AI‑powered manufacturing stack.

Responsibilities:

* Architect and complete large-scale custom model training and fine-tuning jobs (SFT, RLHF) on multi-node, multi-GPU clusters.

* Optimize training throughput and memory efficiency using distributed training strategies (FSDP, Deep Speed, Megatron-LM) and mixed-precision techniques (FP16/BF16).

* Design and develop autonomous AI Agents capable of multi-step reasoning, planning, and tool execution to automate complex manufacturing workflows.

* Analyze and profile complex workloads (e.g., LLM training, Rendering pipelines) to identify bottlenecks in compute, memory bandwidth, and latency.

* Write and optimize high-performance kernels using CUDA, HIP, or custom assembly (PTX/SASS) to unlock hardware capabilities.

* Collaborate with Hardware Architects to define features for next-generation GPUs based on workload characterization.

* Design and implement performance regression testing suites to catch degradations in drivers or compilers.

* Mentor engineers who are developing skills in parallel programming paradigms and optimization techniques.

Minimum Qualifications:

* 10+ years of experience with deep expertise in GPU architecture (memory hierarchy, tensor cores, NVLink) and GPU resource management across cloud and on‑prem environments, along with 5+ years in performance optimization, parallel computing, and low‑level systems using C++ and GPGPU frameworks (CUDA preferred; HIP/OpenCL/Metal acceptable).

* Strong hands-on experience building scalable ML systems, including distributed training (DDP, FSDP), model parallelism, and end-to-end automation of training, testing, and deployment workflows.

* Deep proficiency in LLMs, including prompt engineering, tool/function calling, chain-of-thought reasoning, fine-tuning with PEFT methods (LoRA, QLoRA), and inference optimization using engines like vLLM and Tensor

RT-LLM.

* Experience developing GenAI applications and AI agents using frameworks such as Lang Chain, Lang Graph, Llama Index, and Auto Gen, with strong knowledge of ML frameworks (PyTorch required; Tensor Flow/scikit-learn a plus).

* Strong programming in Python (preferred) or Java, with experience in CI/CD and cloud-native tools (Git, Jenkins, Docker, Kubernetes); excellent communication skills, fast-paced delivery mentality, and a Bachelor's or Master's degree in Computer Science, Statistics, or a related field.

Preferred Qualifications:

* Ph.D. in Computer Science, Statistics, or related field (or equivalent experience), with strong foundations in mathematics, probability, statistics, and algorithms.

* Experience with HPC job schedulers (e.g., Slurm) and orchestrating large-scale GPU workloads on Kubernetes using tools such as Ray and Kubeflow.

* Strong expertise in CUDA programming, Triton kernels, and developing custom C++ extensions for PyTorch to optimize and accelerate workloads.

* Experience designing and coordinating multi-agent systems, including collaboration between specialized agents in complex architectures.

* Proven ability to product ionize data science solutions,…