Research and Engineering Intern

About the Role

NVIDIA is seeking an outstanding Research & Engineering Intern to join the Agentic Systems Intelligence (ASI) team. This team is at the forefront of research and engineering, advancing high-performance AI and building the next generation of agentic AI systems for the CUDA ecosystem. The work spans the full stack, from training and improving models to designing agent architectures and multi-agent systems, and building the systems, runtimes, and evaluation frameworks for large-scale effectiveness.

Key Responsibilities

• Research, build, and optimize agentic AI systems for the CUDA ecosystem, including agent architectures, multi-agent workflows, tool use, memory, and orchestration.
• Develop data pipelines, environments, verification tools, reward mechanisms, and evaluation frameworks to improve model and agent capabilities through rigorous experimentation.
• Co-design and optimize agentic systems across NVIDIA’s software and hardware stack, connecting advances in models and agents with compilers, runtimes, libraries, kernels, and GPUs. Demonstrate measurable improvements in correctness, performance, or developer productivity.

Requirements & Tech Stack

• Strong foundations in one or more of the following: AI systems, agentic AI, machine learning, reinforcement learning, high-performance computing, or computer architecture.
• Strong Python programming skills and proficiency in C/C++ or Rust, with solid software engineering fundamentals.
• Experience with GPU computing and an interest in performance optimization and working across system boundaries.

Qualifications

• Pursuing a Master’s or PhD in Computer Science, Electrical Engineering, or a similar field.
• Hands-on research or development experience in agent systems, coding agents, model training, reinforcement learning, or modern AI inference systems.

Ways to Stand Out

• PhD research in a relevant area, with demonstrated ability to formulate research questions, build working implementations, and evaluate them rigorously.
• Experience building coding agents, autonomous software-engineering systems, or agent training and evaluation environments.
• Hands-on experience with GPU programming and performance optimization, or with systems such as TRT-LLM, SGLang, or vLLM.
• Evidence of technical depth and ownership through impactful research prototypes, deployed systems, or substantial open-source contributions. Relevant publications are welcome; strong engineering and measurable real-world impact are especially valued.