Fall 2026
Software Engineer Intern
AI Tools
Posted on 5/16/2026
NVIDIA
10,001+ employees
Designs GPUs and AI HPC platforms
Compensation Overview
$20 - $71/hr
Company Historically Provides H1B Sponsorship
Santa Clara, CA, USA
In Person
 Software Engineering (1) |
|---|
- Pursuing a BS, MS or PhD with a focus on Computer Science, Computer Engineering, or a related field
- Conceptual understanding of LLMs, Generative AI, and core Machine Learning concepts
- Demonstrated passion or basic project experience with Agentic AI concepts (e.g., RAG, MCP, multi-step planning, or using frameworks like LangChain/LlamaIndex)
- Familiarity with software development guidelines, including version control (Git/GitHub) and basic testing/debugging
- Problem solving and analytical thinking, with strong algorithmic, design and debugging skills
- Proficient in at least one programming language such as C, C++, C#, Java, or Python (Python preferred)
- Experience with writing and consuming APIs, and working with web application frameworks
- Strong verbal and written communication skills
- Curiosity about different areas of software engineering and an eagerness to leave your comfort zone
- An eagerness for learning and working on the cutting edge of technology
- Design and rapidly prototype LLM-powered agents that exhibit planning, reasoning, memory, and tool-use capabilities to automate complex, multi-step tasks
- Craft, refine, and test high-performance prompts, instructions, and system messages to guide agent behavior and optimize task completion accuracy
- Work with agent orchestration frameworks to connect agents with internal and external APIs, databases, and knowledge bases (e.g., Vector DBs for RAG) to enable real-time actions
- Develop metrics and meticulous testing workflows to benchmark agent performance, reliability, and safety across various use cases
- Maintain clear user documentation of agent architectures, workflows, and experimental results
- Stay ahead with the latest developments in generative AI, agent architectures, and multi-agent systems, and propose innovative applications for business challenges
- Collaborations: supporting thousands of developers, working for billion-dollar business lines - with responsiveness, thoroughness and collaboration
NVIDIA designs and manufactures graphics processing units (GPUs) and computing platforms used for gaming, data centers, and artificial intelligence. These products work by using parallel processing to handle complex mathematical calculations much faster than standard computer processors, supported by a software ecosystem that allows developers to build and run AI models. Unlike competitors that may focus solely on hardware, NVIDIA integrates its chips with specialized software and cloud services to create a complete environment for high-performance tasks. The company’s goal is to provide the underlying technology necessary to power advanced computing, from realistic video game graphics to autonomous vehicles and large-scale data analysis.
Company Size
10,001+
Company Stage
IPO
Headquarters
Santa Clara, California
Founded
1993
Simplify's Take
What believers are saying
- Hyperscaler capital spending near $700 billion supports continued GPU and networking demand.
- Blue Yonder's Nemotron-based factory shows NVIDIA can monetize vertical AI workflows.
- China H200 approvals restore some revenue despite export-control constraints.
What critics are saying
- Amazon, Alphabet, and Microsoft keep replacing GPUs with proprietary AI chips.
- U.S. export controls can abruptly cut off China revenue and force product downgrades.
- At $5.46 trillion, any Q1 guide-down can trigger severe multiple compression.
What makes NVIDIA unique
- CUDA and the full-stack platform create strong developer lock-in across AI workloads.
- NVIDIA spans gaming, data center, automotive, robotics, and professional visualization.
- Dell Deskside Agentic AI extends NVIDIA into secure on-premises enterprise deployments.
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Growth & Insights and Company News
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