美商半導體大廠｜MTS AI Engineer｜彈性上下班 x 年薪4.5M以上-Ting

Responsibilities include, but not limited to:

• 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.
• 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.

Requirements

Minimum Qualifications:

• 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, scikit-learn, etc.).
• Software development skills and the desire to work on cutting edge development in a Cloud environment.
• Strong scripting and programming skills in one of the following, Python or Java (Python preferred).
• Experience with continuous integration/continuous delivery (CI/CD) tools (Jenkins, Git, Docker, Kubernetes).
• 5+ years of experience in performance optimization, parallel computing, or low-level systems programming.
• Deep expertise in C++ and at least one GPGPU framework (CUDA is preferred, but HIP/OpenCL/Metal are acceptable).
• Outstanding analytical thinking, interpersonal, oral and written communication skills.
• Ability to prioritize and meet critical project timelines in a fast-paced environment.

Preferred:

• 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.