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Research and Engineering Intern, Agentic Systems Intelligence
NVIDIA. Research, build, and optimize agentic AI systems for the CUDA ecosystem, including agent architectures, multi-agent workflows, tool use, memory, and orchestration .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in AI systems, particularly agentic AI, with strong programming skills in Python and C/C++. Proven ability to optimize performance across NVIDIA's software and hardware stack, leveraging GPU computing and modern AI inference systems.
Highest-signal resume keywords
Agentic AI Systems DevelopmentPython ProgrammingGPU ComputingReinforcement LearningPerformance Optimization
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI SystemsMachine LearningReinforcement LearningHigh-Performance ComputingC/C++ ProgrammingRust ProgrammingSoftware Engineering FundamentalsAgent TrainingModel TrainingGPU Programming
Soft Skills
CollaborationResearch SkillsProblem-Solving
Tools & Technologies
CUDATRT-LLMSGLangVLLM
Industry Keywords
Agent ArchitecturesMulti-Agent WorkflowsEvaluation FrameworksOrchestrationAutonomous Software-Engineering Systems
Tech Stack
Tools & technologiesPythonRustC++
About the role
Key responsibilities & impact- 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
- Investigate open-ended research questions and turn promising ideas into working systems
- Collaborate closely with NVIDIA software, model, and hardware teams
- Bring new capabilities into NVIDIA products
Requirements
What you’ll need- Pursuing a Master’s or PhD in Computer Science, Electrical Engineering, or a similar field
- Strong foundations in AI systems, agentic AI, machine learning, reinforcement learning, high-performance computing, or computer architecture
- Hands-on research or development experience in agent systems, coding agents, model training, reinforcement learning, or modern AI inference systems
- Strong Python programming skills
- Proficiency in C/C++ or Rust
- Solid software engineering fundamentals
- Experience with GPU computing
- Interest in performance optimization and working across system boundaries
- PhD research in a relevant area is a way to stand out
- Experience building coding agents, autonomous software-engineering systems, or agent training and evaluation environments is a way to stand out
- Hands-on experience with GPU programming and performance optimization, or TRT-LLM, SGLang, or vLLM is a way to stand out
- Evidence of technical depth and ownership through impactful research prototypes, deployed systems, or substantial open-source contributions is a way to stand out
Benefits
Comp & perks- Competitive salaries
- Generous benefits package
- Intern benefits