Research and Engineering Intern
NVIDIA
This job at a glance
- Who can apply
- 30–32 years of experience
- Must know
- ETLMachine learningPythonC++CRust
- Work
- On-site in Austin · internship
- Pay
- Not stated by the employer.
- Skill demand
- Python21%Machine learning12%C++7%ETL3%of 100 similar entry-level jobs posted in the US this week
- Listed
- Nov 7, 2026 · still open
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About the Role
NVIDIA is looking for an outstanding Research & Engineering Intern to join the Agentic Systems Intelligence (ASI) team. This team is focused on advancing high-performance AI and building the next generation of agentic AI systems for the CUDA ecosystem.
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.
- 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 From The Crowd
- 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.