Research Intern, Efficient Deep Learning - 2027

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NVIDIA is searching for an outstanding PhD intern working on efficient deep learning to join the Deep Learning Efficiency Research (DLER) team. We are passionate about research that pushes boundaries but also has impact in the real world. The team has two core focuses: (1) efficient diffusion language models and multimodal generative models, and (2) efficient agentic AI with hybrid inference orchestration across cloud and edge. We are also excited about post-training model optimization (pruning, quantization, NAS), efficient architecture design, adaptive/dynamic inference, and resource-efficient training and finetuning. You will work within an amazing and collaborative research team that consistently publishes at the top venues in computer vision and machine learning. Our existing expertise includes computer vision, deep learning, generative models, diffusion LLMs, multimodal models, and hybrid cloud–edge agentic systems. Your contributions have the chance to create real impact on our products. What you'll be doing: Research, design, and implement novel methods for efficient deep learning in one or both of the team’s focus areas: Diffusion LLMs and multimodal models — sampling efficiency, adaptive unmasking, self-speculation / parallel decoding, training and distillation pipelines, and multimodal generation. Efficient agentic AI — hybrid inference orchestration across cloud and edge, routing and scheduling policies, on-device vs. cloud expert delegation, and resource-aware agent loops. Publish original research. Collaborate with other team members and teams. Work with product groups to transfer technology. Collaborate with external researchers. What we need to see: Pursuing a Ph.D. in Computer Science/Engineering, Electrical Engineering, etc. Excellent knowledge of theory and practice of machine learning and deep learning. Experience with large language models, diffusion language models, multimodal / vision-language models, or agentic systems is required. Hands-on experience with large-scale model training including data preparation and model parallelization (tensor and pipeline) is required. Outstanding research track record with at least one top-tier conference (ICML, ICLR, NeurIPS, CVPR, ICCV, etc.). Excellent communication skills. Ways to stand out from the crowd: Parallel programming (e.g., CUDA). Interest or experience in hybrid cloud–edge inference, orchestration, or adaptive routing. Background in pruning, quantization, NAS, or efficient backbones. NVIDIA is widely considered to be one of the technology world’s most desirable employers with competitive salaries and a generous benefits package, we have some of the most forward-thinking and hardworking people in the world working for us. And, due to unprecedented growth, our best-in-class engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for computer architecture and technology, we want to hear from you! Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience. The hourly rate for our interns is 38 USD - 94 USD. You will also be eligible for Internbenefits.​ Applications for this job will be accepted at least until October 9, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. Originally posted on Himalayas

NVIDIA正在寻找一位优秀的博士实习生，从事高效深度学习方向的研究，加入深度学习效率研究（DLER）团队。我们热衷于推动边界的研究，同时也注重在现实世界中产生影响。该团队有两个核心关注点：（1）高效扩散语言模型和多模态生成模型，以及（2）跨云和边缘的高效智能体AI与混合推理编排。我们还对训练后模型优化（剪枝、量化、NAS）、高效架构设计、自适应/动态推理以及资源高效的训练和微调充满热情。你将在一个卓越且协作的研究团队中工作，该团队持续在计算机视觉和机器学习领域的顶级会议上发表论文。我们现有的专业知识包括计算机视觉、深度学习、生成模型、扩散LLM、多模态模型以及混合云-边缘智能体系统。你的贡献有机会对我们的产品产生真正的影响。你将要做的工作：在团队的一个或两个重点领域中研究、设计并实现高效深度学习的新方法：扩散LLM和多模态模型——采样效率、自适应去掩码、自推测/并行解码、训练和蒸馏流水线以及多模态生成。高效智能体AI——跨云和边缘的混合推理编排、路由和调度策略、设备端与云端专家委托以及资源感知的智能体循环。发表原创研究。与其他团队成员和团队协作。与产品团队合作进行技术转移。与外部研究人员合作。我们希望你具备：正在攻读计算机科学/工程、电气工程等专业的博士学位。具备出色的机器学习和深度学习理论与实践知识。要求具有大语言模型、扩散语言模型、多模态/视觉-语言模型或智能体系统的经验。要求具有大规模模型训练的实践经验，包括数据准备和模型并行化（张量和流水线）。出色的研究记录，至少在一家顶级会议（ICML、ICLR、NeurIPS、CVPR、ICCV等）发表过论文。出色的沟通能力。脱颖而出的方式：并行编程（例如CUDA）。对混合云-边缘推理、编排或自适应路由有兴趣或经验。具备剪枝、量化、NAS或高效骨干网络方面的背景。NVIDIA被广泛认为是技术世界中最令人向往的雇主之一，提供有竞争力的薪资和丰厚的福利待遇，我们拥有世界上最具前瞻性和最勤奋的人才为我们工作。而且，由于前所未有的增长，我们一流的工程团队正在迅速壮大。如果你是一位富有创造力和自主性的工程师，对计算机架构和技术充满真正的热情，我们希望听到你的声音！我们的实习时薪是根据职位、你的地点、学年、学位和经验制定的标准薪酬。我们实习生的时薪为38美元至94美元。你还将有资格享受实习生福利。该职位的申请将至少接受至2026年10月9日。此职位为现有空缺。NVIDIA在其招聘流程中使用AI工具。NVIDIA致力于营造包容性的工作环境，并自豪地成为平等机会雇主。由于我们高度重视当前和未来员工的多样性，我们不会基于种族、宗教、肤色、国籍、性别、性别表达、性取向、年龄、婚姻状况、退伍军人身份、残疾状况或任何其他受法律保护的特征进行歧视（包括在我们的招聘和晋升实践中）。最初发布于Himalayas

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