MTS - Machine Learning

1100 Micron SemiAsiaOP Pte Ltd; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

Micron Technology in Singapore seeks a Senior Machine Learning Engineer (MTS) to architect and deploy large-scale models, including GenAI and autonomous AI Agents, across multi-node GPUs and optimized hardware.

You will optimize training throughput, build distributed data pipelines, integrate with cloud data warehousing, and collaborate with cross-functional teams to accelerate AI-driven manufacturing workflows.

Proficiency with PyTorch, PEFT (LoRA/QLoRA), LangChain, and CI/CD is essential.

Qualifications

• Technical Degree required.
• Computer Science or Statistics background desirable.
• Deep understanding of GPU architecture and distributed training.
• Experience with DDP, FSDP, and model parallelism.
• Proficiency in fine-tuning LLMs and optimizing inference engines.
• Experience with GenAI applications and AI Agents.

Responsibilities

• Architect and execute large-scale model training and fine-tuning on multi-node, multi-GPU clusters.
• Optimize training throughput and memory with distributed strategies and mixed precision.
• Design and develop autonomous AI Agents for multi-step workflows.
• Implement Agentic frameworks to orchestrate LLMs with internal APIs and tools.
• Profile and debug GPU bottlenecks to maximize hardware utilization.
• Build data and solution pipelines feeding ML models and GenAI apps.

Skills

GPU architecture

DDP

PEFT LoRA/QLoRA

LangChain

LangGraph

LlamaIndex

AutoGen

PyTorch

TensorRT-LLM

CI/CD

Education

Technical Degree

Computer Science or Statistics background

Tools

LangChain

LangGraph

LlamaIndex

AutoGen

PyTorch

CI/CD tools (Jenkins, Docker, Kubernetes)

Job description

Job Overview

The Smart Manufacturing and AI team at Micron Technology is looking for an ambitious Machine Learning Engineer (Member of Technical Staff | MTS). Our mission is to deliver industry‑winning machine learning, custom GenAI, and Agentic AI solutions to power Micron’s dominance in the highly competitive memory solutions market.

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, Google Cloud) to enable AI/ML and Agentic solutions.
• Create and maintain CI/CD pipelines of machine learning and AI Agent solutions in the cloud.

Education Qualifications

• Technical Degree required.
• Computer Science or Statistics background highly desired.

Minimum Qualifications

• Deep understanding of GPU architecture (memory hierarchy, tensor cores, interconnects such as 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 such as LangChain, LangGraph, LlamaIndex, or AutoGen.
• Proficiency with Large Language Models (LLMs), including prompt engineering, function calling/tool use, and Chain‑of‑Thought reasoning.
• Experience building and executing end‑to‑end ML systems that automate training, testing, and deployment of machine learning models.
• Familiarity with machine learning frameworks (PyTorch required; TensorFlow, scikit‑learn, etc.).
• Software development skills and a desire to work on cutting‑edge development in a cloud environment.
• Strong scripting and programming skills in either Python or Java (Python preferred).
• Experience with continuous integration/continuous delivery (CI/CD) tools (Jenkins, Git, Docker, Kubernetes).
• 10+ years building scalable ETL pipelines.
• 10+ years of experience with big data processing and/or developing applications and data sources.
• Outstanding analytical thinking, interpersonal, oral and written communication skills.
• Ability to prioritize and meet critical project timelines in a fast‑paced environment.

Preferred Qualifications

• Experience with HPC job schedulers (e.g., Slurm) or orchestrating GPU workloads on Kubernetes (Ray, KubeFlow).
• Knowledge of lower‑level optimization (CUDA programming, Triton kernels, or custom C++ extensions for PyTorch).
• Experience with Multi‑Agent Systems and orchestrating collaboration between specialized agents.
• Deep knowledge of math, probability, statistics, and algorithms.
• Demonstrated ability to study and transform data science prototypes into production solutions.
• Knowledge of computer vision and/or signal processing, including techniques for classification and feature extraction.

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

To request assistance with the application process and/or for reasonable accommodations, please contact [e-mail].