Summer 2026
PhD Research Intern
Generative AI
Posted on 6/20/2026
NVIDIA
10,001+ employees
Designs GPUs and AI HPC platforms
Compensation Overview
$30 - $94/hr
Company Historically Provides H1B Sponsorship
Santa Clara, CA, USA
In Person
On-site in Santa Clara, California.
 AI & Machine Learning |
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- Currently pursuing a PhD in CS, EE, Robotics, or related fields.
- Strong background in generative AI, computer vision, multimodal learning, robotics, or reinforcement learning.
- Prior publication record and research experience.
- Strong Python and PyTorch skills.
- Conduct research in generative AI, multimodal foundation models, world models, and embodied AI.
- Develop algorithms for video understanding/generation, action-conditioned simulation, multimodal reasoning, and policy learning.
- Train and evaluate large-scale models using video, image, language, and robotics or autonomous driving data.
- Collaborate with researchers and engineers across AI, robotics, simulation, and graphics teams.
- Publish research at top conferences and transfer innovations into NVIDIA products.
- Experience with large-scale foundation model training.
- Research in video models, VLMs, world models, robotics, or autonomous driving.
- Experience with distributed training, simulation, or embodied AI.
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
Your Connections
People at NVIDIA who can refer or advise you
Simplify's Take
What believers are saying
- Blackwell and Rubin refreshes can drive another enterprise upgrade cycle.
- Hyperscaler demand for AI infrastructure expands GPU, networking, and interconnect revenue.
- Jetson, robotics, and edge AI broaden NVIDIA beyond data centers.
What critics are saying
- Hyperscalers build custom accelerators, reducing dependence on NVIDIA GPUs.
- China restrictions and domestic rivals like Huawei keep shrinking addressable demand.
- Rubin execution slips open room for AMD and custom ASIC competitors.
What makes NVIDIA unique
- CUDA and full-stack hardware-software integration create high switching costs.
- Spectrum-X and NVLink Fusion extend control beyond GPUs into networking.
- Annual architecture upgrades sustain first-mover advantage across AI, gaming, and robotics.
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Company Equity
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Growth & Insights and Company News
6 month growth
↑ 0%1 year growth
↓ -3%2 year growth
↓ -3%Mistral AI has raised €1.7 billion in a Series C funding round at an €11.7 billion post-money valuation. The round was led by semiconductor equipment manufacturer ASML Holding, with participation from existing investors including DST Global, Andreessen Horowitz, Bpifrance, General Catalyst, Index Ventures, Lightspeed and NVIDIA. The Paris-based AI company will use the funding to advance its scientific research and develop custom decentralised frontier AI solutions for complex engineering and industrial problems. ASML CEO Christophe Fouquet said the partnership aims to generate benefits for ASML customers through AI-enabled products and solutions. Mistral AI CEO Arthur Mensch stated the investment will help address engineering challenges in the semiconductor and AI value chain whilst maintaining the company's independence.
With funding led by Radical Ventures, Decart is building the infrastructure layer for the next generation of low-latency AI systems, through three product lines: DOS, an ultra-optimized inference and training stack that enables agents and reasoning models to run smarter and faster; and the models Lucy, its World Model for Immersive Experiences; and Oasis, its World Model for Physical AI – both powered by DOS. Today, we’re also announcing DOS 2.0, with new versions of Lucy and Oasis launching in the coming weeks.
Matlantis has integrated NVIDIA's ALCHEMI Toolkit into its materials simulation platform to accelerate industrial materials discovery. The company previously incorporated NVIDIA Warp-optimised kernels, achieving up to 10x speed improvements in atomistic calculations. The integration includes LightPFP, Matlantis' lightweight potential for large-scale simulations, which uses a server-based architecture with NVIDIA ALCHEMI Toolkit-Ops to reduce communication bottlenecks. Matlantis plans to integrate its flagship Universal Machine-Learning Interatomic Potential with the toolkit to further enhance GPU efficiency. Launched in 2021, Matlantis is a cloud-based atomistic simulator jointly developed by PFN and ENEOS. The platform uses deep learning to increase simulation speeds by tens of thousands of times and serves over 150 companies discovering materials including catalysts, batteries and semiconductors.
Nvidia shares have climbed 18% over a ten-day winning streak, the longest since 2023. The stock is trading about 8% below its October all-time high of $212.19. CEO Jensen Huang revealed at last month's GTC conference that Nvidia has over $1 trillion in GPU orders through 2027, including Blackwell and next-generation Vera Rubin chips. Data centre revenue surged 75% year-over-year and now comprises 88% of the business, a dramatic shift from five years ago when gaming dominated. The rally follows major deals including Meta's February commitment to deploy millions of Nvidia chips across its global data centres. On Monday, Nvidia denied rumours it was pursuing acquisitions of PC makers Dell or HP. The company also unveiled Ising, a new family of open-source models for quantum computing.