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東京都の機械学習エンジニアの求人198件応募資格・募集要項・仕事内容を見る
Distributed Training & Inference Optimization Engineer (LLM) - GPU Optimization Department (GPUOD)
- 給与
- 記載なし
- 勤務地
- 東京都
- 休日
- 記載なし
- 雇用形態
- 記載なし
どんな仕事かAIによる生成
The GPU Training & Inference Optimization Engineer focuses on maximizing the performance, efficiency, and scalability of LLM training and inference workloads on Rakuten’s GPU clusters. The role optimizes distributed training frameworks and inference engines, improves GPU utilization, reduces training time and inference latency, and collaborates with infrastructure teams on large-scale GPU workloads.
この求人に応募
応募資格
必須
- 3+ years of hands-on experience in GPU-accelerated ML training & inference optimization, preferably for LLMs or large-scale deep learning models.
- Deep expertise in PyTorch, DeepSpeed, FSDP, or Megatron-LM, with experience in distributed training optimizations.
- Strong knowledge of LLM inference optimizations (e.g., quantization, pruning, KV caching, continuous batching).
- Bachelor’s or higher degree in Computer Science, Engineering, or related field.
- English (総合 - 3 - 上級)
歓迎
- Proficiency in CUDA, Triton kernel, NVIDIA tools (Nsight, NCCL), and performance profiling (e.g., PyTorch Profiler, TensorBoard).
- Experience with LLM-specific optimizations (e.g., FlashAttention, PagedAttention, LoRA, speculative decoding).
- Familiarity with Kubernetes (K8s) for GPU workloads (e.g., KubeFlow, Volcano).
- Contributions to open-source ML frameworks (e.g., PyTorch, DeepSpeed, vLLM).
- Experience with inference serving frameworks (e.g., vLLM, TensorRT-LLM, Triton, Hugging Face TGI).
募集要項最終確認 11/12
- 職種
- 機械学習エンジニア
- 雇用形態
- 記載なし
- 給与
- 記載なし
- 勤務地
- 東京都, 東京都
- 働き方
- 記載なし
- 経験年数
- 3年以上
- 学歴
- 大学卒業以上
- 掲載日
- 2026/07/26 17:05（110日前）
- 最終確認
- 2026/11/12 21:35（1日前）
仕事内容
Department Overview
- GPU Optimization Department (GPUOD) is responsible for the strategic management, optimization, and governance of Rakuten's company-wide AI infrastructure, ensuring high-performance, cost-efficient utilization of compute resources for machine learning workloads. We oversee a large-scale hybrid infrastructure spanning thousands of accelerators, including the latest Hopper and upcoming Blackwell architectures.
- Optimize compute resource allocation across on-premises and multi-cloud environments, maximizing efficiency for training and inference workloads.
- Manage hybrid orchestration of diverse accelerator resources, ensuring seamless scalability and cost-effective deployment.
- Develop and enhance frameworks for large-scale distributed training, with special focus on LLMs and generative AI.
- Optimize inference performance through model optimization techniques and system-level acceleration.
- Collaborate with internal teams to deliver scalable, high-availability inference services tailored to business needs.
- Continuously evaluate next-generation hardware solutions, including specialized AI chips optimized for LLM workloads.
- By effectively managing both conventional and specialized compute resources across on-premises and cloud environments, our team ensures Rakuten's AI ecosystem remains at the forefront of performance, reliability, and cost-efficiency.
Why We Hire
- Work on cutting-edge LLM training & inference optimization at scale.
- Directly impact Rakuten’s AI infrastructure by improving efficiency and reducing costs.
- Collaborate with global AI/ML teams on high-impact challenges.
- Opportunity to research and implement state-of-the-art GPU optimizations.
Position Details
- As a GPU Training & Inference Optimization Engineer, you will focus on maximizing the performance, efficiency, and scalability of LLM training and inference workloads on Rakuten’s GPU clusters. You will deeply optimize training frameworks (e.g., PyTorch, DeepSpeed, FSDP) and inference engines (e.g., vLLM, TensorRT-LLM, Triton, SGLang), ensuring Rakuten’s AI models run at peak efficiency.
- This role requires strong expertise in GPU-accelerated ML frameworks, distributed training, and inference optimization, with a focus on reducing training time, improving GPU utilization, and minimizing inference latency.
Key Responsibilities
- Optimize LLM training frameworks (e.g., PyTorch, DeepSpeed, Megatron-LM, FSDP) to maximize GPU utilization and reduce training time.
- Profile and optimize distributed training bottlenecks (e.g., NCCL issues, CUDA kernel efficiency, communication overhead).
- Implement and tune inference optimizations (e.g., quantization, dynamic batching, KV caching) for low-latency, high-throughput LLM serving (vLLM, TensorRT-LLM, Triton, SGLang).
- Collaborate with infrastructure teams to improve GPU cluster scheduling, resource allocation, and fault tolerance for large-scale training jobs.
- Develop benchmarking tools to measure and improve training throughput, memory efficiency, and inference latency.
- Research and apply cutting-edge techniques (e.g., mixture-of-experts, speculative decoding) to optimize LLM performance.
この求人は、楽天グループ株式会社の採用ページの内容をAIが読み取って整理したものです。応募の前に採用ページの原文を確かめてください。データの集め方企業サイト
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- 機械学習エンジニア274
- 東京都の機械学習エンジニア198
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- AIエンジニア491
- データエンジニア468