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Graph Neural Network Jobs in Florida (NOW HIRING)

Cisco

Applied AI Scientist

Tampa, FL · 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 ...

Cisco

Applied AI Scientist

Miami, FL · 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 ...

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Graph Neural Network information

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.

What are popular job titles related to Graph Neural Network jobs in Florida?

For Graph Neural Network jobs in Florida, the most frequently searched job titles are:

What job categories do people searching Graph Neural Network jobs in Florida look for?

The top searched job categories for Graph Neural Network jobs in Florida are:

What cities in Florida are hiring for Graph Neural Network jobs?

Cities in Florida with the most Graph Neural Network job openings:

Graph Neural Network jobs near you
Infographic showing various Graph Neural Network job openings in Florida as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 11% Part Time, and 5% Contract. Highlights an 91% Physical, 2% Hybrid, and 7% Remote job distribution.

TSI/SSBI/SCI - AI/ML -Software Engineer - Sensing Systems - Sarasota.FL

Sarasota, FL • On-site

Henpen Corporation
See more jobs at Henpen Corporationexternal

$100 - $150/hr

Other

Posted 7 days ago

Job description

TSI/SSBI/SCI - AI/ML -Software Engineer - Sensing Systems - Sarasota.FL
  • Sarasota, Florida

Must be TSI/SSBI/SCI to move forward. if not, your application will not be considered.

Sarasota, Florida, USA

Job Description

We are looking for an engineer with a solid foundation in artificial intelligence and machine learning applications to help us solve challenging problems related to signal processing.

The right candidate will have a high degree of drive and dedication, and the ability to learn quickly, work well within a team, and hit the ground running.

Qualifications
  • BS degree or higher in Computer Science, Electrical Engineering, Computer Engineering, Mathematics, or related field
  • Minimum 1-year hands‑on experience in AI or ML in a professional environment (3-5 years preferred)
  • Strong knowledge of machine learning model development, deployment, and modern ML libraries (TensorFlow, PyTorch, scikit-learn, etc.)
  • Solid programming background with experience using statistical and signal analysis libraries
  • Experience with neural network architectures including deep learning models
  • Understanding of transformer architectures and attention mechanisms
  • Strong understanding of MLOps, deployment and processing pipelines, testing/validation
  • TS/SCI active clearance required.
  • U.S. Citizenship required
Nice to have, but not required:
  • Understanding of digital signal processing fundamentals
  • Experience with RFML
  • Experience with Large Language Models (LLMs) including fine-tuning and prompt engineering
  • Knowledge of AI applications for autonomous decision-making and analysis
  • Additional consideration for experience with multimodal, agentic systems using RAG, COT, or MARL approaches
  • Experience with reinforcement learning, human feedback, and related system learning methods
  • Experience creating and deploying containerized AI models with Docker/Kubernetes
  • Working with cloud AI platforms (AWS Bedrock, Azure OpenAI, Google Vertex AI)
  • Experience with model monitoring, A/B testing, and performance optimization
  • Experience with real-time inference systems and low-latency model serving
  • Knowledge of adversarial ML and AI security/robustness techniques
  • Experience with graph neural networks for network analysis
  • Experience in design, deployment, support of AI or ML model for significant real-world applications
Job Duties:
  • You will be responsible for designing, developing, and implementing AI/ML solutions for a wide range of decision-making and SIGINT processing needs.
  • This includes working with time-series data and developing models for event characterization, pattern recognition, anomaly detection, decision making, and automated analysis of SIGINT sensor systems.
  • You will work with team leads to integrate AI/ML capabilities into enterprise architectures, ensuring performant processing while considering aspects of accuracy, security, and maintainability.
  • This also includes enabling autonomous decision-making systems that can operate with minimal human intervention, creating adaptive processing systems for dynamic environments, and discovering features and inferring system states from the underlying data streams.
  • You will work towards solutions for large-scale sensing systems, implementing tailored models deliver intelligent insights in support of critical Intelligence Community and Department of Defense missions.

FULL RELOCATION plus Industry best benefits & stock.

Why is This a Great Opportunity

We are looking for an engineer with a solid foundation in artificial intelligence and machine learning applications to help us solve challenging problems related to signal processing.

The right candidate will have a high degree of drive and dedication, and the ability to learn quickly, work well within a team, and hit the ground running.

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