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Graph Neural Network Internship Jobs (NOW HIRING)

Photon

Gen AI Lead - TX

Irving, TX

$15.50 - $18.75/hr

Design and architect complex Gen AI solutions leveraging Large Language Models (LLMs), Transformer/Neural Network models, and Vector/Graph Databases (Pinecone, Milvus, Neo4j). * Oversee the ...

Photon

Gen AI Lead - TX

Irving, TX · On-site

$15.50 - $18.75/hr

Design and architect complex Gen AI solutions leveraging Large Language Models (LLMs), Transformer/Neural Network models, and Vector/Graph Databases (Pinecone, Milvus, Neo4j). * Oversee the ...

Avride

Lead AI Infrastructure Engineer

Austin, TX · On-site

$101K - $133K/yr

The vast part of the execution graph is implemented as a chain of neural network operations. The onboard mode relies on stable latencies of the inference of those networks, while in simulation we ...

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How much do graph neural network internship jobs pay per hour?

As of Aug 21, 2026, the average hourly pay for graph neural network internship in the United States is $17.44, according to ZipRecruiter salary data. Most workers in this role earn between $14.42 and $19.23 per hour, depending on experience, location, and employer.

What is a graph neural network internship?

A Graph Neural Network (GNN) Internship is a position designed for students or recent graduates to gain hands-on experience working with GNNs, a type of deep learning model that processes data structured as graphs. Interns typically participate in research, model development, and the application of GNNs to various problems such as social network analysis, recommendation systems, or molecular property prediction. The internship provides opportunities to collaborate with experienced researchers, learn cutting-edge techniques, and contribute to real-world projects involving graph-based machine learning.

What types of projects or tasks can I expect to work on during a graph neural network internship?

As a Graph Neural Network (GNN) intern, you will typically be involved in projects such as developing and optimizing GNN models for real-world datasets, implementing new neural network architectures, and conducting experiments to evaluate model performance. You may also assist with data preprocessing, feature engineering, and collaborating with data scientists and machine learning engineers to integrate GNN solutions into larger systems. Regular tasks include reviewing recent research, documenting findings, and presenting your results to the team. This internship offers an excellent opportunity to deepen your understanding of advanced machine learning methods while gaining hands-on experience in a collaborative research-focused environment.

What are the key skills and qualifications needed to thrive as a graph neural network intern, and why are they important?

To thrive as a Graph Neural Network Intern, you need a solid background in machine learning, data science, and programming languages such as Python, often supported by coursework or research experience in deep learning and graph theory. Familiarity with frameworks like PyTorch Geometric, TensorFlow, and libraries such as NetworkX, along with experience using Jupyter Notebooks and Git, is typically expected. Strong analytical thinking, problem-solving skills, and effective communication help interns collaborate with research teams and convey complex ideas clearly. These skills and qualifications are essential for contributing to cutting-edge AI projects and advancing research in graph-based machine learning.
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Infographic showing various Graph Neural Network Internship job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 81% Full Time, 12% Part Time, and 6% Contract. Highlights an 93% Physical, 2% Hybrid, and 5% Remote job distribution, with an average salary of $36,265 per year, or $17.4 per hour.

Software Engineer II: AI Compiler Engineer

Cadence Design Systems Inc

Austin, TX • On-site

$96K - $132K/yr

Other

This job post has expired today. Applications are no longer accepted.

Cadence Design Systems rating

9.0

Company rating: 9.0 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

33rd of 245 rated software companies

Job description

At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.
Job Description
Cadence Design Systems Inc. is looking for a motivated Software Engineer II: AI Compiler Engineer to work with us.
As a Software Engineer II: AI Compiler Engineer you will work with complex high performance SoC's, and is one of the best kept secrets within the semi IP world powering AR/VR, HiFi Audio and Speech, Vision, Imaging and hundreds of intelligent IoT applications.
Be a part of a team that develops an AI graph compiler that takes as input Neural Networks (NNs) created in frameworks such as PyTorch or TensorFlow and converts them into optimized code suitable for execution on special-purpose and embedded platforms.
Cadence is also a Fortune 100 Best Companies to Work For.
Job Description:
  • Developing a deep learning compiler stack that takes neural network descriptions (CNNs/RNNs) created in frameworks such as Caffe, PyTorch, TensorFlow, etc. and converts them into code suitable for execution on special-purpose and embedded platforms.
  • Use modern compiler frameworks such as LLVM and MLIR.
  • Developing optimized implementations of a variety of neural-network operations and integrating them into a runtime framework
  • Developing new optimization techniques and algorithms to efficiently map CNNs onto a wide range of Xtensa processors and specialized hardware.
  • Benchmarking end-to-end network performance on a variety of DSP and special-purpose accelerator platforms.
  • Enhancing the framework to improve overall functionality and performance on the various hardware platforms.
  • Devising multiprocessor/multicore partitioning and scheduling strategies.
  • Developing complex programs to validate the functionality and performance of the CNN application programming kit.
  • Working with hardware designers to identify opportunities for additional hardware acceleration of neural network functions.
  • Working with industry-leading partners and customers to design and standardize neural network APIs..

Requirements:
  • Complete Bachelor in Computer Science or Computer Engineering or equivalent experience.
  • A high level of C and C++ programming expertise with 3-5+ years of experience is required.
  • Expertise in software development on Linux and Windows systems including test, debug and release is required.
  • Knowledge of and experience with a state-of-the-art compiler stack such as LLVM and MLIR.
  • Experience implementing compilation techniques such loop optimization, polyhedral models, and IR construction/transition/lowering techniques.

Nice to have:
  • Master or PhD.
  • 3+ years of experience working on a production compiler is highly desired.
  • Python experience highly desired
  • Prior work with CNNs and familiarity with deep learning frameworks (TensorFlow, Caffe/2, etc.) is a strong plus
  • Experience programming and optimizing for embedded platforms such as DSPs with DMA engines highly desired
  • Familiarity with the state-of-the-art deep learning compilation approaches (Glow, TVM, XLA, etc.) is a plus
  • Familiarity with various deep learning networks and their applications (Classification/Segmentation/Object Detection/RNNs) is a plus
  • Knowledge of neural net exchange formats (ONNX, NNEF) is a plus

Additional Job Details:
  • Employment term: 40 hours/week.
  • Hybrid work.
  • Competitive benefits.

Cadence is the only company that provides the expertise and tools, IP, and hardware required for the entire electronics design chain, from chip design to chip packaging to boards and to systems. We enable electronic systems and semiconductor companies to create innovative products that transform the way people live, work, and play. Our products are used in mobile, consumer, cloud datacenter, automotive, aerospace, IoT, industrial and other market segments.
For more information, access ;br>We're doing work that matters. Help us solve what others can't.

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