Member of Technical Staff, AI Engineering

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 junior engineers on parallel programming paradigms and optimization techniques.

• Technical Degree required. Ph.D. in Computer Science or Statistics background highly desired.
• Deep understanding of GPU architecture (memory hierarchy, tensor cores, NVLink), GPU resource management (cloud & on‑prem), 5+ years in performance optimization/parallel computing/low‑level systems, and deep expertise in C++ with GPGPU frameworks (CUDA preferred; HIP/OpenCL/Metal acceptable).
• Hands‑on experience with DDP, FSDP, model parallelism, and building end‑to‑end ML systems that automate training, testing, and deployment.
• Proficiency with LLMs including prompt engineering, function calling/tool use, CoT reasoning; fine‑tuning using PEFT techniques (LoRA, QLoRA); and optimizing inference engines (vLLM, 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 ML framework knowledge (PyTorch required; Tensor Flow, scikit‑learn, etc.).
• Strong programming or scripting skills in Python (preferred) or Java. Experience with CI/CD and cloud‑native tools such as Git, Jenkins, Docker, and Kubernetes is essential.

• Experience with HPC job schedulers (e.g., Slurm) and orchestrating large‑scale GPU workloads on Kubernetes using tools like Ray and Kubeflow.
• Knowledge of CUDA programming, Triton kernels, and building custom C++ extensions for PyTorch to accelerate workloads.
• Experience designing and orchestrating collaboration between specialized agents in multi‑agent architectures.
• Deep knowledge of mathematics, probability, statistics, and algorithms.
• Demonstrated ability to evolve data science prototypes into production systems, with knowledge of computer vision and/or signal processing techniques for classification and feature extraction.

Micron offers a choice of medical, dental, and vision plans, income protection benefits, paid family leave, paid time off, paid holidays, and other programs to support personal wellbeing and professional growth.

Micron is proud to be an equal opportunity workplace and is an affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, age, national origin, citizenship status, disability, protected veteran status, gender identity or any other factor protected by applicable federal, state, or local laws.