安克创新AI Engineer Intern

We are looking for a Generative AI Intern Engineer to join the NVIDIA Developer Technology group (Devtech) and work with a team of experienced engineers on innovative uses of AI for games and content creation. The Devtech team works with NVIDIA researchers and leading game developers to bring cutting edge AI research from across NVIDIA and the industry to gamers and 3D professionals in high performance packages such as real-time inferenced graphics, physics and animations. What you’ll be doing: • Research and implement innovative generative AI algorithms for game engines and authoring tools, including real-time neural graphics, physics based animation and diffusion models. • Develop neural graphics, animation and physics models and maintain open-source projects for both game-making and user runtimes. Integrate them into mainstream game engines and DCC tools. • Use various optimization techniques, such as tensor fusion and quantization, to fit the AI models onto user devices and maximize the performance of inference for real-time gaming. • Collaborate with game developers on optimizations and improvements for specific GenAI applications. • Interact closely with the architecture and driver teams at NVIDIA in ensuring the best possible experience on current generation hardware, and on determining trends and features for next generation architectures.

An exciting internship opportunity to make an immediate contribution to AMD's next generation of technology innovations awaits you! We have a multifaceted, high-energy work environment filled with a diverse group of employees, and we provide outstanding opportunities for developing your career. During your internship, our programs provide the opportunity to collaborate with AMD leaders, receive one-on-one mentorship, attend amazing networking events, and much more. Being part of AMD means receiving hands-on experience that will give you a competitive edge. Together We Advance your career! JOB DETAILS: Location: Shanghai Onsite/Hybrid: This role requires the student to work full time (40 hours a week), either in a hybrid or onsite work structure throughout the duration of the co-op/intern term. Duration: January 1, 2026 - June 30, 2026 WHAT YOU WILL BE DOING: We are seeking highly motivated AI/ML Engineering Intern to join our AMD Research team. In this role: You will develop machine learning models to optimize GPU power/performance tradeoffs using real-world silicon data. We will train you to deploy models via MLOps pipelines for AMD’s internal tools. Your responsibility will include analyzing hardware telemetry data (power, thermal, clocks) to identify efficiency bottlenecks. You will collaborate with hardware engineers to validate models on next-gen AMD GPUs. Learning Outcomes: Master GPU-accelerated ML workflows. Gain hands-on experience with industrial-scale MLOps.

As an intern, you will: Design and prototype AI-driven tools that support hardware engineers in data analysis, test automation, and debugging workflows. Implement backend services in Python and frontend interfaces in TypeScript (React/Next.js). Integrate databases (SQL, vector databases) for data storage, search, and retrieval. Explore AI/ML workflows (embeddings, retrieval, summarization, code assistance). Collaborate with hardware and software engineers to translate requirements into working tools. Write clear documentation, tests, and deployment scripts.

As a Machine Learning (ML) Engineer, you will be entrusted with the critical role of innovating and applying state-of-the-art research in ML to tackle complex data problems. The solutions you develop will significantly impact future Apple products and the broader ML development ecosystem. You will work with a multidisciplinary team to actively participate in the data-model co-design and co-development practice. Your responsibilities will extend to the design and development of a comprehensive data curation framework. You will also create robust model evaluation pipelines, integral to the continuous improvement and assessment of ML models. Additionally, your role will entail an in-depth analysis of collected data to underscore its influence on model performance. Furthermore, you will have the opportunity to showcase your groundbreaking research work by publishing and presenting at premier academic venues. Your work may span a variety of topics, including but not limited to: * Designing and implementing semi-supervised, self-supervised representation learning techniques for maximizing the power of both limited labeled data and large-scale unlabeled data. * Developing evaluation protocols centered on the end-to-end user experience, with a focus on anticipating potential failure modes, edge cases, and anomalies. * Employing data selection techniques such as novelty detection, active learning, and core-set selection for diverse data types like images, 3D models, natural language, and audio. * Uncovering patterns in data, setting performance targets, and leveraging modern statistical and ML-based methods to model data distributions. This will aid in reducing redundancy and addressing out-of-distribution samples.