{"isCandidateLoggedIn": false, "isProfileRegistered": false, "isAllowedForRegistration": true, "registrationNotAllowedReason": null, "candidateLoginMandatory": false, "eventData": {"name": "NVIDIA Warsaw Virtual Event 2026", "groupId": "nvidia.com", "plannedEventEncId": "qZbodMmBY", "createdByName": "Kane Sidhu", "institution": "Copernicus Science Centre", "venue": "", "address": "https://drive.google.com/file/d/1ZWMRaZRYpVDTYfAefXJ0E0LLUqPaLup7/view", "description": "<h3>Like no place you\u2019ve ever worked.. Our team is composed of driven, innovative professionals dedicated to pushing the boundaries of technology. Can\u2019t wait to see what\u2019s next?</h3><div><br></div><h1><strong><u><a href=\"https://nvidia.eightfold.ai/events/candidate/registration?plannedEventId=qZbodMmBY\" rel=\"noopener noreferrer\" target=\"_blank\">Register your interest in NVIDIA and we can contact you about roles that match your experience when they open*</a></u></strong></h1><div><strong><u>*</u></strong>I agree that NVIDIA may submit internship applications on my behalf for roles it considers suitable for my profile. I understand I will be informed of any such application and may withdraw this consent at any time</div><div><br></div><h3><strong style=\"color: rgb(34, 34, 34); background-color: rgb(255, 255, 255);\">When you register, choose from the following areas of interest:</strong></h3><div><strong>1) CUDA Math Device Libraries</strong></div><ul><li data-list=\"bullet\"><span class=\"ql-ui\" contenteditable=\"false\"></span><strong>Accelerating Math device APIs on the latest NVIDIA GPUs</strong> \u2013 Enhance GPU-accelerated Math device APIs (MathDx - cuBLASDx, cuFFTDx, cuSOLVERDx, nvCOMPDx, NPPDx, cuRANDDx) and build AI-powered tools to help users integrate and maximize the performance of their applications on the latest NVIDIA GPUs.</li></ul><div><strong>2) Accelerated Processing for Data and Computer Vision</strong></div><ul><li data-list=\"bullet\"><span class=\"ql-ui\" contenteditable=\"false\"></span><strong>Accelerated Data Loading </strong>-Accelerated Data Loading Solutions for Deep Learning in Computer Vision and Life Sciences (C++, Python &amp; CUDA)</li><li data-list=\"bullet\"><span class=\"ql-ui\" contenteditable=\"false\"></span><strong>Accelerating data and image compression </strong>- Accelerating data and image compression on GPUs using established and innovative algorithms</li></ul><div><strong>3) Visual Generative AI Optimization</strong></div><ul><li data-list=\"bullet\"><span class=\"ql-ui\" contenteditable=\"false\"></span><strong>Cosmos - generative world foundation models</strong> - Efficient Inference of Frontier Physical AI Models for Autonomous Vehicles and Robotics</li><li data-list=\"bullet\"><span class=\"ql-ui\" contenteditable=\"false\"></span><strong>Frontier Open Visual GenAI models optimization: from checkpoint to production</strong> - Explore state-of-the-art and research new optimization techniques for Visual GenAI models</li></ul><div><strong>4) LLM/VLM Compression &amp; Inference Efficiency</strong></div><ul><li data-list=\"bullet\"><span class=\"ql-ui\" contenteditable=\"false\"></span><strong>A framework for hardware-aware LLM/VLM optimization (quantization/pruning/nas/distillation/spec dec)</strong> - Develop an opensource framework for hardware-aware optimization of LLM/VLM models via quantization/pruning/nas/distillation/spec dec</li><li data-list=\"bullet\"><span class=\"ql-ui\" contenteditable=\"false\"></span><strong>Algorithms for hardware-aware LLM/VLM optimization (quantization/pruning/nas/distillation/spec dec)</strong> - Build hardware-aware algorithms for LLM/VLM optimization via quantization/pruning/nas/distillation/spec dec</li><li data-list=\"bullet\"><span class=\"ql-ui\" contenteditable=\"false\"></span><strong>Fast KV Cache Compression for Offloading </strong>- Desgin a fast KV/MLA cache compression method, primarily for vLLM/SGLang L1--L2 (host--device) offloading: higher compression than nvComp's gANS, and faster than KV Cache Transform Coding (KVTC).</li></ul><div><strong>5) VLM Training for Document Understanding</strong></div><ul><li data-list=\"bullet\"><span class=\"ql-ui\" contenteditable=\"false\"></span><strong>VLM model for document parsing and understanding</strong> \u2013 Research and build a state-of-the-art VLM model for OCR (nemotron-parse)</li></ul><div><strong>6) AI Benchmarking Platform</strong></div><ul><li data-list=\"bullet\"><span class=\"ql-ui\" contenteditable=\"false\"></span><strong>Beyond Passing Tests: Measuring AI-Assisted Software Quality </strong>-<strong> </strong>Build a Python analysis tool that quantifies complexity, modularity, maintainability, and over-engineering, then use it to compare alternative AI-assisted implementations of real features in systems</li><li data-list=\"bullet\"><span class=\"ql-ui\" contenteditable=\"false\"></span><strong>Integrate SOTA models with benchmarking platform </strong>- Bring and evaluate newest AI models in training, inference and RL on latest, often unreased NVIDIA hardware: scale from 1 to 1000+ GPUs</li><li data-list=\"bullet\"><span class=\"ql-ui\" contenteditable=\"false\"></span><strong>Predicting GPU memory use of compressed AI models across inference engines </strong>- Today the only way to know whether a compressed model fits on a GPU is to run it and watch for a crash; the intern will measure real memory use across engines and settings, then build a tool that predicts it.</li></ul><div><strong>7) AI Agent Evaluation and Safety</strong></div><ul><li data-list=\"bullet\"><span class=\"ql-ui\" contenteditable=\"false\"></span><strong>Detecting and preventing agent cheating in NeMo Gym evaluations </strong>-<strong> </strong>Build detectors and harness controls that catch and block agents passing Imogen tasks by tampering with tests, graders, or the network rather than solving them, validated by red-teaming against an agent instructed to cheat.</li><li data-list=\"bullet\"><span class=\"ql-ui\" contenteditable=\"false\"></span><strong>Swappable agent harnesses for NeMo Gym evaluations </strong>- Define a common agent adapter interface in Imogen and implement it for several off-the-shelf agents, so the same tasks can be run with the model fixed and the scaffold varied to show where a score actually comes from.</li></ul><div><strong>8) Nsight Developer Tools</strong></div><ul><li data-list=\"bullet\"><span class=\"ql-ui\" contenteditable=\"false\"></span><strong>System Performance Insights </strong>-<strong> </strong>Design and implement a new data-collection or analysis capability for diagnosing operating-system resource usage and runtime behavior in NVIDIA Nsight Systems.</li></ul><div><strong>9) Transformer Engine</strong></div><ul><li data-list=\"bullet\"><span class=\"ql-ui\" contenteditable=\"false\"></span><strong>Implement and optimize training support for novel attention variants </strong>-<strong> </strong>Use CuTe DSL to implement and optimize attention operation for emerging Transformer architectures and integrate them into Transformer Engine.</li><li data-list=\"bullet\"><span class=\"ql-ui\" contenteditable=\"false\"></span><strong>Fine-grained communication\u2013computation overlap in Transformer training</strong> -<strong> </strong>Use NCCL\u2019s device-initiated communication primitives to accelerate Transformer training by fusing operations and enabling fine-grained overlap of communication with computation.</li><li data-list=\"bullet\"><span class=\"ql-ui\" contenteditable=\"false\"></span><strong>Implement and optimize Transformer Engine kernels using cuTile - 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