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Graph Neural Network Jobs (NOW HIRING)

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 ...

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 ...

STR

Lead Algorithm & Signal Processing Engineer

Woburn, MA · On-site

$157K - $220K/yr

Machine learning tasks such as natural language processing, image recognition, semantic segmentation, reinforcement learning, approaches such as Bayesian, deep convolutional and graph neural network ...

STR

Lead Algorithm & Signal Processing Engineer

Dayton, OH · On-site

$157K - $220K/yr

Machine learning tasks such as natural language processing, image recognition, semantic segmentation, reinforcement learning, approaches such as Bayesian, deep convolutional and graph neural network ...

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How much do graph neural network jobs pay per hour?

As of Jul 14, 2026, the average hourly pay for graph neural network in the United States is $26.64, according to ZipRecruiter salary data. Most workers in this role earn between $22.60 and $29.09 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Graph Neural Network position, and why are they important?

To excel as a Graph Neural Network Engineer, you need a strong background in machine learning, graph theory, neural networks, and proficiency in programming languages such as Python. Familiarity with deep learning frameworks like PyTorch or TensorFlow, and experience with specialized libraries such as DGL or PyTorch Geometric are highly valued. Excellent problem-solving skills, teamwork, and the ability to communicate complex concepts to both technical and non-technical stakeholders will help you stand out. These combined abilities enable professionals to design, implement, and deploy cutting-edge GNN models that address complex, real-world data-structure challenges across various industries.

What does a typical project workflow look like for a Graph Neural Network Engineer?

A typical project workflow for a Graph Neural Network Engineer involves collaborating with data scientists and domain experts to understand the problem, preprocessing and visualizing graph-structured data, and selecting appropriate model architectures. The role often includes building, training, and evaluating GNN models, iterating on hyperparameters, and deploying models to production environments. Throughout the process, you will engage in code reviews, document findings, and present results to stakeholders. Teamwork and effective communication are essential, as projects frequently require close collaboration with researchers, software engineers, and business units to ensure solutions meet practical needs and performance goals.

What is a Graph Neural Network job?

A Graph Neural Network (GNN) job typically involves designing, implementing, and optimizing neural network models that operate on graph-structured data. Professionals in this role apply GNNs to tasks like recommendation systems, fraud detection, social network analysis, and molecular property prediction. Responsibilities often include data preprocessing, model architecture selection, training, evaluation, and deployment. Strong knowledge of machine learning, deep learning frameworks (such as PyTorch or TensorFlow), and graph theory is essential.

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Graph Neural Network jobs near you
Infographic showing various Graph Neural Network job openings in the United States as of July 2026, with employment types broken down into 19% Locum Tenens, 45% Full Time, 19% Part Time, 2% Contract, 14% Nights, and 1% Summer. Highlights an 60% Physical, 2% Hybrid, and 38% Remote job distribution, with an average salary of $55,420 per year, or $26.6 per hour.
Software Engineer II: AI Compiler Engineer

Software Engineer II: AI Compiler Engineer

Cadence Design Systems Inc.

Austin, TX • Hybrid

$96K - $132K/yr

Full-time

Posted 29 days ago

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 http://www.cadence.com

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