AI/ML Software Engineer - TS/SCI

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.

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.

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, 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, 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.