仕事内容●Business Overview
AI&Data Division (AIDD) spearheads data science&AI initiatives by leveraging data from Rakuten Group. We build a platform for large-scale field experimentations using cutting-edge technologies to provide critical insights that enable faster and better and faster contribution for our business. Our division boasts an international culture created by talented employees from around the world. Following the strategic vision“Rakuten as a data-driven membership company”, AIDD is expanding its data&AI related activities across multiple Rakuten Group companies.
●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.
●As a central enabler for AI innovation, we:
- 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.
経験・資格●Mandatory Qualifications:
- 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.
●Desired Qualifications:
- 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).
勤務地
想定年収
福利厚生・待遇
勤務条件標準勤務時間帯 9:00～17:30 所定労働時間7.5時間、休憩時間1時間です。
※楽天グループ朝会実施日の就業時間は8:00～16:30となります。
※一部のポジションでは、企画業務型裁量労働制または専門業務型裁量労働制の適用対象となる場合があります。
※一部、フレックスタイム制を適用しています。コアタイム：11:00～15:00（朝会実施日は8:00～12:00）
●休日／完全週休2日制（土、日）・祝日
●休暇／夏季休暇・年末年始休暇・年次有給休暇・特別休暇など
●昇給：年2回、6月・12月に会社実績や本人の評価により見直しを行います。
●賞与：年2回、6月・12月に会社及び個人の業績により支給します。
厚生年金保険、健康保険、労災保険、雇用保険など
Recruiting No.
社名
事業内容
【東証プライム上場 有名電機メーカーグループ】 技術系総合職
【東証プライム上場 完成車メーカー】 プロダクト企画部 デジタルプロダクト開発における商品企画・プロダクトマネジメント
【東証プライム上場 日本有数の電機メーカー系プライムベンダー】 ITエンジニア（社会インフラのDX/GX事業 電力・エネルギー領域）
西武グループのダイナミックな成長を、不動産ファンドの運用から推進します。
全員参加型のビジネス変革が成果を生み出し、キャリア人材の成長機会が増え続けています。
日本企業の長期的な成長を支える、新しい金融の仕組み作りに挑戦中です
人々の生活や命を支えるため、「食料・水・環境」分野で地域に根ざした事業にチャレンジする
オルタナティブ市場成長の担い手として。個が経験を活かし、チームワークで価値を生み出す運用会社です。
大企業から中堅中小企業まで。 サステナビリティの視点で ビジネスの成長ストーリーを描く。
