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Graph Neural Network Jobs (NOW HIRING)

Dayhoff Labs

AI Research Scientist

Cambridge, MA · On-site

$150 - $230/hr

Experience in ML-for-chemistry or ML-for-biology (e.g., neural network potentials, graph neural networks, protein or reaction models) Logistics Compensation is highly competitive. We're also able to ...

Dayhoff Labs

AI Research Engineer

Cambridge, MA · On-site

Experience in ML-for-chemistry or ML-for-biology (e.g., neural network potentials, graph neural networks, protein or reaction models) Logistics Compensation is highly competitive. We're also able to ...

Workday

Senior/Principal AI Engineer

Seattle, WA · On-site

$142K - $196K/yr

... and/or graph neural network models for real-world use cases * 6+ years of proven experience with cloud computing platforms (e.g. AWS, GCP, etc.) * Proven track record of successfully leading ...

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How much do graph neural network jobs pay per hour?

As of Aug 24, 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 is a graph neural network?

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.

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 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.

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Graph Neural Network jobs near you
Infographic showing various Graph Neural Network 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 $55,420 per year, or $26.6 per hour.

Graph Neural Network Influenza Modeling Intern

Boston Public Health Commission

Boston, MA • On-site

Internship

Posted 12 days ago

Job description

The Graph Neural Network (GNN) Influenza Modeling Intern will support the development and evaluation of machine learning models to improve seasonal influenza forecasting. The intern will analyze historical and current influenza surveillance data, develop and validate forecasting models using Graph Neural Networks, and assess how integrating multiple public health data sources-including emergency department visits, hospitalizations, laboratory testing, immunizations, and wastewater surveillance-affects predictive accuracy. The intern will also build reproducible R and/or Python workflows, support model visualization and deployment, and document processes to ensure long-term sustainability of the forecasting model.
Learning Objectives
  • Gain experience with influenza surveillance systems and public health data sources.
  • Learn and compare traditional forecasting methods with machine learning and Graph Neural Network approaches.
  • Develop and evaluate forecasting models using R and/or Python.
  • Build reproducible analytical workflows and visualizations for public health applications.
  • Document methodologies and support knowledge transfer to BPHC staff.

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