1

Graph Neural Network Jobs in Washington, DC (NOW HIRING)

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 ...

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 ...

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 ...

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 ...

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 ...

next page

Showing results 1-20

Graph Neural Network information

See Washington, DC salary details

$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 Washington, DC is $30.18, according to ZipRecruiter salary data. Most workers in this role earn between $25.58 and $32.93 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 Washington, DC? The most popular types of Graph Neural Network jobs in Washington, DC are:
Infographic showing various Graph Neural Network job openings in Washington, DC as of July 2026, with employment types broken down into 57% Full Time, and 43% Part Time. Highlights an 49% In-person, and 51% Remote job distribution, with an average salary of $62,768 per year, or $30.2 per hour.

Lead Algorithm & Signal Processing Engineer

STR

Arlington, VA • On-site

$157K - $220K/yr

Other

Posted 6 days ago


Job description

About the Team:

The Electronic Warfare and Novel Capabilities Group (EWNC) develops and delivers advanced signal processing algorithms and prototype systems for next-generation radar and electronic warfare technologies.

EWNC fosters a culture of innovation and collaboration. We investigate cutting edge technologies and interface with engineers across sectors and divisions at STR and researchers at academic institutions and industry partners to develop practical and effective solutions to RF sensing and counter-sensing challenges. If you're seeking an opportunity to apply your deep technical knowledge and creativity to make a lasting impact, we invite you to join us.

The Role:

As a Lead Algorithm & Signal Processing Researcher, you'll have the opportunity to develop state-of-the-art algorithms and techniques for sense making and sensor decision making, from signal detection and classification, to electronic support measures, attack, and protection, to cognitive EW and more.

What You Will Do:

  • Design and apply cutting-edge signal processing and machine learning techniques to solve complex radar and RF sensing and counter-sensing challenges
  • Design and implement models and simulations for advanced RF systems
  • Lead small teams and projects
  • Support business development and proposal writing efforts
  • Collaborate with diverse teams to deploy cutting-edge capabilities to prototype systems

Who You Are:

  • This position requires the ability to obtain a Top Secret security clearance, for which U.S. citizenship is needed by U.S. Government
  • BS, MS, or PhD in Electrical Engineering, Applied Mathematics, Physics, or related technical discipline
  • 5-7+ years of relevant work experience depending on degree (BS +7 years or MS +5 years)
  • Experience in two or more of the following areas:
    • RF/radar signal processing, optimization, waveform design, electronic warfare, machine learning, adaptive signal processing, AI/ML algorithm development, algorithm development, test & evaluation, model & simulation, or systems engineering, test engineering, radar modelling, or RF systems prototyping
  • Proficiency in one or more scientific or mathematical programming languages, such as MATLAB, Python, and C/C++
  • Strong mathematical, troubleshooting, written, and verbal communication skills

Even Better:

  • Active Security Clearance
  • Experience with:
    • RF technologies in the defense sector, particularly for sensing and electronic warfare applications including electronic attack, electronic protection, cognitive EW, and advanced electronic support measures techniques and algorithms
    • Machine learning tasks such as natural language processing, image recognition, semantic segmentation, reinforcement learning, approaches such as Bayesian, deep convolutional and graph neural network methods, and tools such as PyTorch, Ray, TensorFlow/board, and MLflow
    • Interacting with decision-makers and customers to translate mission needs into an end-to-end analytical solution
  • Ability to apply novel, innovative and interdisciplinary approaches to challenging engineering problems
  • Ability to create and present briefings to customers and senior management to provide both high-level and deep technical solution explanations

Join EWNC and help shape the future of RF sensing and electronic warfare by developing innovative technologies that transition from advanced research into operational prototype systems. While this position is ideally suited for a Lead Engineer, applicants whose qualifications closely align with the requirements are encouraged to apply. We have opportunities for talented engineers at multiple experience levels in our MA, VA, and OH office. 

Pay Information
Full-Time Salary Range: $157,000 - $220,000

The salary range listed is based on external market data. Offers are based on factors, such as but not limited to, the candidate's experience, education, training, key skills/critical skills, security clearances, and prevailing market and business conditions.