Software Engineer at Contour Education, creating impactful software solutions for students. Collaborating on full - stack features and enhancing the Learning Portal experience.
About the role
- GPU Performance Engineer in Micron's Smart Manufacturing and AI team. Focusing on large-scale modeling, optimization, and deployment of AI solutions in their memory solutions.
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.
- 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
- 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.
Benefits
- Competitive salary
- Flexible working hours
- Professional development budget
- Home office setup allowance
- Global team events
Job title
Job type
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Degree requirement
Tech skills
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