Applied AI Researcher
Kirkland, WA · On-site
... Graph Neural Network (GNN) architectures to model complex relationships, knowledge graphs, recommendation systems, and structured data representations. • Evaluate emerging research and rapidly ...
Applied AI Researcher
Kirkland, WA · On-site
... Graph Neural Network (GNN) architectures to model complex relationships, knowledge graphs, recommendation systems, and structured data representations. • Evaluate emerging research and rapidly ...
Applied AI Researcher
Kirkland, WA · On-site
Research and implement Graph Neural Network (GNN) architectures to model complex relationships, knowledge graphs, recommendation systems, and structured data representations. * Evaluate emerging ...
Applied AI Researcher
Kirkland, WA · On-site
Research and implement Graph Neural Network (GNN) architectures to model complex relationships, knowledge graphs, recommendation systems, and structured data representations. * Evaluate emerging ...
We deliver user interfaces, agentic AI agents, temporal graph neural network outage prediction, and data streaming solutions. We are looking for a talented FEE II to help design and develop Network ...
Posted today
We deliver user interfaces, agentic AI agents, temporal graph neural network outage prediction, and data streaming solutions. We are looking for a talented FEE II to help design and develop Network ...
Posted today
We deliver user interfaces, agentic AI agents, temporal graph neural network outage prediction, and data streaming solutions. We are looking for a talented FEE II to help design and develop Network ...
Posted today
We deliver user interfaces, agentic AI agents, temporal graph neural network outage prediction, and data streaming solutions. We are looking for a talented FEE II to help design and develop Network ...
Posted today
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 ...
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 ...
... scale graph neural networks (GNNs) and link analysis models to power Identity Graph, fraud ... text, network/graph data, and third-party identity signals. • Demonstrated ability to drive ...
... scale graph neural networks (GNNs) and link analysis models to power Identity Graph, fraud ... text, network/graph data, and third-party identity signals. • Demonstrated ability to drive ...
Applied AI Scientist
Bellevue, WA · On-site
Our work spans networking, security, observability, and customer experience - designing and ... Large-scale graph representation learning and Graph Neural Networks (GNNs) (e.g., GCN/GAT/GraphSAGE ...
Applied AI Scientist
Bellevue, WA · On-site
Our work spans networking, security, observability, and customer experience - designing and ... Large-scale graph representation learning and Graph Neural Networks (GNNs) (e.g., GCN/GAT/GraphSAGE ...
Applied AI Scientist
Seattle, WA · On-site
Our work spans networking, security, observability, and customer experience - designing and ... Large-scale graph representation learning and Graph Neural Networks (GNNs) (e.g., GCN/GAT/GraphSAGE ...
Applied AI Scientist
Seattle, WA · On-site
Our work spans networking, security, observability, and customer experience - designing and ... Large-scale graph representation learning and Graph Neural Networks (GNNs) (e.g., GCN/GAT/GraphSAGE ...
Principal AI Engineer
Seattle, WA · On-site
... 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 ...
Principal AI Engineer
Seattle, WA · On-site
... 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 ...
... 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 ...
... 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 ...
Applied Scientist II - AMZ9971140
Seattle, WA · On-site
$153K - $193K/yr
Extend and apply deep learning architectures (e.g., graph neural networks, transformers, recurrent ... network configuration verification, resource compliance checking, and system reliability assurance.
Applied Scientist II - AMZ9971140
Seattle, WA · On-site
$153K - $193K/yr
Extend and apply deep learning architectures (e.g., graph neural networks, transformers, recurrent ... network configuration verification, resource compliance checking, and system reliability assurance.
Graph Neural Network information
See Seattle, WA salary details
$16.96 - $19.45
2% of jobs
$19.45 - $21.93
8% of jobs
$21.93 - $24.42
9% of jobs
$25.55 is the 25th percentile. Wages below this are outliers.
$24.42 - $26.91
11% of jobs
The median wage is $28.86 / hr.
$26.91 - $29.40
24% of jobs
$29.40 - $31.88
19% of jobs
$32.19 is the 75th percentile. Wages above this are outliers.
$31.88 - $34.37
8% of jobs
$34.37 - $36.86
6% of jobs
$36.86 - $39.34
4% of jobs
$39.34 - $41.83
4% of jobs
$41.83 - $44.32
3% of jobs
$16
$30
$44
How much do graph neural network jobs pay per hour?
As of Aug 5, 2026, the average hourly pay for graph neural network in Seattle, WA is $30.32, according to ZipRecruiter salary data. Most workers in this role earn between $25.72 and $33.08 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?
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 are the most commonly searched types of Graph Neural Network jobs in Seattle, WA? The most popular types of Graph Neural Network jobs in Seattle, WA are:
What are popular job titles related to Graph Neural Network jobs in Seattle, WA? For Graph Neural Network jobs in Seattle, WA, the most frequently searched job titles are:
What job categories do people searching Graph Neural Network jobs in Seattle, WA look for? The top searched job categories for Graph Neural Network jobs in Seattle, WA are:
What cities near Seattle, WA are hiring for Graph Neural Network jobs? Cities near Seattle, WA with the most Graph Neural Network job openings:
Job description
Job Summary:
Electronic Arts (EA) creates next-level entertainment experiences that inspire players and fans globally. They are seeking a Senior AI/ML Research Scientist to advance Generative AI capabilities, focusing on developing and optimizing state-of-the-art AI models for creative content generation.
Responsibilities:
• Lead research and development of state-of-the-art generative AI systems, including diffusion models, latent diffusion architectures, and multimodal foundation models.
• Collaborate with stakeholders to understand business needs and translate them into model requirements. Advise on what is possible and the impact that the models can drive.
• Design, train, fine-tune, and optimize large-scale AI models for creative content generation.
• Develop and apply parameter-efficient adaptation techniques, including LoRA, adapters, prompt tuning, and related methods for foundation model customization.
• Advance internal LLM capabilities through model training, fine-tuning, evaluation, alignment, and optimization.
• Research and implement Graph Neural Network (GNN) architectures to model complex relationships, knowledge graphs, recommendation systems, and structured data representations.
• Evaluate emerging research and rapidly prototype innovative approaches from leading conferences and publications.
• Collaborate with engineering teams to transition research innovations into scalable production systems.
Qualifications:
Required:
• PhD or Master's in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field.
• 7+ years of experience conducting advanced machine learning research and development with a track record of translating cutting-edge research into impactful products or platforms.
• Strong publication record in leading AI conferences or journals (NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP, KDD, AAAI, or equivalent).
• Deep expertise in deep learning fundamentals, optimization, representation learning, and neural network architectures.
• Strong programming skills in Python and experience with modern ML frameworks such as PyTorch, JAX, or TensorFlow.
• Experience in training and evaluating large-scale machine learning models on distributed compute infrastructure and GPU-accelerated computing environments.
Preferred:
• Expertise in developing and training diffusion models, latent diffusion models, or related generative architectures.
• Experience fine-tuning foundation models using LoRA, QLoRA, adapters, PEFT techniques, RLHF, DPO, or similar approaches.
• Experience developing generative AI systems for image, video, audio, 3D, or other creative content domains.
Company:
Electronic Arts creates next-level entertainment experiences that inspire players and fans around the world. Here, everyone is part of the story. Founded in 1982, the company is headquartered in Redwood City, USA, with a team of 10001+ employees. The company is currently Late Stage.
Electronic Arts (EA) creates next-level entertainment experiences that inspire players and fans globally. They are seeking a Senior AI/ML Research Scientist to advance Generative AI capabilities, focusing on developing and optimizing state-of-the-art AI models for creative content generation.
Responsibilities:
• Lead research and development of state-of-the-art generative AI systems, including diffusion models, latent diffusion architectures, and multimodal foundation models.
• Collaborate with stakeholders to understand business needs and translate them into model requirements. Advise on what is possible and the impact that the models can drive.
• Design, train, fine-tune, and optimize large-scale AI models for creative content generation.
• Develop and apply parameter-efficient adaptation techniques, including LoRA, adapters, prompt tuning, and related methods for foundation model customization.
• Advance internal LLM capabilities through model training, fine-tuning, evaluation, alignment, and optimization.
• Research and implement Graph Neural Network (GNN) architectures to model complex relationships, knowledge graphs, recommendation systems, and structured data representations.
• Evaluate emerging research and rapidly prototype innovative approaches from leading conferences and publications.
• Collaborate with engineering teams to transition research innovations into scalable production systems.
Qualifications:
Required:
• PhD or Master's in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field.
• 7+ years of experience conducting advanced machine learning research and development with a track record of translating cutting-edge research into impactful products or platforms.
• Strong publication record in leading AI conferences or journals (NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP, KDD, AAAI, or equivalent).
• Deep expertise in deep learning fundamentals, optimization, representation learning, and neural network architectures.
• Strong programming skills in Python and experience with modern ML frameworks such as PyTorch, JAX, or TensorFlow.
• Experience in training and evaluating large-scale machine learning models on distributed compute infrastructure and GPU-accelerated computing environments.
Preferred:
• Expertise in developing and training diffusion models, latent diffusion models, or related generative architectures.
• Experience fine-tuning foundation models using LoRA, QLoRA, adapters, PEFT techniques, RLHF, DPO, or similar approaches.
• Experience developing generative AI systems for image, video, audio, 3D, or other creative content domains.
Company:
Electronic Arts creates next-level entertainment experiences that inspire players and fans around the world. Here, everyone is part of the story. Founded in 1982, the company is headquartered in Redwood City, USA, with a team of 10001+ employees. The company is currently Late Stage.
About Electronic Arts
Sourced by ZipRecruiter
Industry
Pc games
Company size
10,000+ Employees
Headquarters location
Redwood City, CA, US
Year founded
1982