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

• 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.
• 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.
• 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.

• 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, with experience in computer vision and/or signal processing for classification and feature extraction.

As a world leader in the semiconductor industry, Micron is dedicated to your personal wellbeing and professional growth.

Micron benefits are designed to help you stay well, provide peace of mind and help you prepare for the future.

We offer a choice of medical, dental and vision plans in all locations enabling team members to select the plans that best meet their family healthcare needs and budget.

Micron also provides benefit programs that help protect your income if you are unable to work due to illness or injury, and paid family leave.

Additionally, Micron benefits include a robust paid time‑off program and paid holidays.

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.