Full-Time
AI Software Engineer New Grad
Kernel Libraries
Posted on 5/19/2026
NVIDIA
10,001+ employees
Designs GPUs and AI HPC platforms
Compensation Overview
$124k - $241.5k/yr
Company Historically Provides H1B Sponsorship
Santa Clara, CA, USA
In Person
 Software Engineering (2) |
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- Masters degree in Computer Science, Electrical Engineering, or related field (or equivalent experience); PhD are preferred
- 2 + years (academic/ industry) experience with ML/DL systems development preferable
- Strong experience in developing or using deep learning frameworks (e.g. PyTorch, JAX, TensorFlow, ONNX, etc) and ideally inference engines and runtimes such as vLLM, SGLang, and MLC.
- Strong Python and C/C++ programming skills
- Innovating and developing new AI systems technologies for efficient inference
- Designing, implementing, and optimizing kernels for high impact AI workloads
- Designing and implementing extensible abstractions for LLM serving engines
- Building efficient just-in-time domain specific compilers and runtimes
- Collaborating closely with other engineers at NVIDIA across deep learning frameworks, libraries, kernels, and GPU arch teams
- Contributing to open source communities like FlashInfer, vLLM, and SGLang
- Background in domain specific compiler and library solutions for LLM inference and training (e.g. FlashInfer, Flash Attention)
- Expertise in inference engines like vLLM and SGLang
- Expertise in machine learning compilers (e.g. Apache TVM, MLIR)
- Strong experience in GPU kernel development and performance optimizations (especially using CUDA C/C++, cuTile, Triton, or similar)
- Open source project ownership or contributions
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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