About the role
AI summarisedMember of Technical Staff (MTS) in Machine Learning at Micron Technology, responsible for architecting and executing large-scale model training, optimizing GPU performance, developing AI agents, and building ML pipelines for GenAI applications in a semiconductor industry context.
IDMFull-timeSmart MFG/AI
Key Responsibilities
- Architect and execute 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, DeepSpeed, 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
- Implement Agentic frameworks (e.g., LangChain, LangGraph, CrewAI) to orchestrate LLM interactions with internal APIs, databases, and software tools
- Profile and debug GPU performance bottlenecks using tools like Nsight Systems or PyTorch Profiler to maximize hardware utilization
- Build and maintain data/solution pipelines that feed machine learning models and GenAI applications
- Design and optimize data structures in data management systems (Snowflake, and Google Cloud platforms) to enable AI/ML and Agentic solutions
- Create/Maintain CI/CD pipelines of machine learning and AI Agent solutions in the cloud
Requirements
- Technical Degree required. Computer Science or Statistics background highly desired
- Deep understanding of GPU architecture (memory hierarchy, tensor cores, interconnects like NVLink) and experience managing GPU resources in both cloud environments and on-prem
- Hands-on experience with Distributed Data Parallel (DDP), Fully Sharded Data Parallel (FSDP), and model parallelism techniques
- Proficiency in fine-tuning Large Language Models using PEFT techniques (LoRA, QLoRA) and optimizing inference engines (vLLM, TensorRT-LLM)
- Experience developing GenAI applications and AI Agents using frameworks like LangChain, LangGraph, LlamaIndex, or AutoGen
- Proficiency with Large Language Models (LLMs), including prompt engineering, function calling/tool use, and Chain-of-Thought (CoT) reasoning
- Experience in building and executing end-to-end ML systems automating training, testing and deploying Machine Learning models
- Familiarity with machine learning frameworks (PyTorch is required
- TensorFlow
- -learn