英伟达Applied Research Intern, Robotics - 2026

As a research intern on the embodied AI projects, you will help build the advanced robotic policies with humanoid robots for the challenging industrial scene — enabling sim-first development, real-world deployment, and continuous learning to make them smarter over time. The ideal candidate will have strong research skills for robotics. What You Will Be Doing: • Bring the latest advancements in embodied AI to simulated and real humanoid robots, successfully apply research in the challenging industrial scenes, like healthcare scenarios. • Build sim-first robot policies with SimReady digital twin and minimize the sim2real gap. • Collaborate across team boundaries to integrate NVIDIA robotics products such as Jetson Thor, Isaac GR00T, Cosmos, and Isaac Sim/Lab into the research work. • Take on a variety of challenges, solve practical research topics. • Deploy and test the developed software on real humanoid robots.

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As an Applied Scientist, you will be responsible for bringing new product designs through to manufacturing. You will work closely with multi-disciplinary groups including Product Design, Industrial Design, Hardware Engineering, and Operations, to drive key aspects of engineering of consumer electronics products. In this role, you will use expertise in physical sciences, theoretical, numerical or empirical techniques to create scalable models representing response of physical systems or devices, including: * Applying domain scientific expertise towards developing innovative analysis and tests to study viability of new materials, designs or processes * Working closely with engineering teams to drive validation, optimization and implementation of hardware design or software algorithmic solutions to improve product and customer risks * Establishing scalable, efficient, automated processes to handle large scale design and data analysis * Conducting research into use conditions, materials and analysis techniques * Tracking general business activity including device health in field and providing clear, compelling reports to management on a regular basis * Developing, implementing guidelines to continually optimize design processes * Using simulation tools like LS-DYNA, and Abaqus for analysis and optimization of product design * Using of programming languages like Python and Matlab for analytical/statistical analyses and automation * Demonstrating strong understanding across multiple physical science domains, e.g. structural, thermal, fluid dynamics, and materials * Developing, analyzing and testing structural solutions from concept design, feature development, product architecture, through system validation * Supporting product development and optimization through application of analysis and testing of complex electronic assemblies using advanced simulation and experimentation tools and techniques

Model Optimization & Deployment: Design and implement efficient workflows for training, distillation, and fine-tuning Small and Large Language Models (SLMs), leveraging techniques such as LoRA, QLoRA, and instruction tuning. Apply model compression strategies—including quantization (e.g., GPTQ, AWQ) and pruning—to reduce inference costs and improve latency. Optimize LLM inference performance using frameworks like vLLM and TensorRT-LLM (TRT-LLM) to enable scalable, low-latency deployment. Build robust and scalable inference systems tailored to heterogeneous production environments, with a strong focus on performance, cost-efficiency, and stability. Evaluation & Data Management： Develop evaluation datasets and metrics to assess model performance in real-world product scenarios. Build and maintain end-to-end machine learning pipelines encompassing data preprocessing, training, validation, and deployment.  Cross-functional Collaboration： Collaborate closely with product managers, engineers, and research scientists to translate business needs into impactful AI solutions, driving real-world adoption and seamless product integration.