AI/ML Software Engineer

• Designing, developing, and implementing AI/ML solutions for a wide range of decision-making and signal processing needs
• Working with time-series data and developing models for event characterization, pattern recognition, anomaly detection, decision making, and automated analysis of signal sensor systems
• Working with team leads to integrate AI/ML capabilities into enterprise architectures, ensuring performant processing while considering aspects of accuracy, security, and maintainability
• 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
• Working towards solutions for large-scale sensing systems, implementing tailored models to deliver intelligent insights in support of critical intelligence community and Department of Defense missions

• 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

• 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 the design, deployment, and support of AI or ML models for significant real-world applications

The company will pay for relocation