About the Role
Develop scalable machine learning pipelines and deploy models into field-ready systems that support autonomous surveillance and threat identification. Work across the full ML lifecycle, from research to production, ensuring robustness and performance in real-world conditions.
Responsibilities
- Design and implement machine learning models for object detection and classification in sensor data
- Optimize models for low-latency inference on edge computing hardware
- Collaborate with software and systems engineers to integrate ML components into larger platforms
- Develop data processing pipelines for large-scale training datasets
- Evaluate model performance using real-world operational metrics
- Improve model accuracy through iterative experimentation and feedback loops
- Deploy models into production environments with monitoring and logging
- Ensure compliance with security and deployment standards for defense systems
- Work with geospatial and temporal data from multiple sensor types
- Support field testing and validation of ML-driven capabilities
- Troubleshoot performance issues in deployed models
- Contribute to model versioning and reproducibility practices
- Develop tools for data labeling and annotation workflows
- Apply domain adaptation techniques to improve generalization across environments
- Maintain documentation for models and training processes
- Assist in defining requirements for new ML-powered features
- Work with simulation environments to augment training data
- Implement model interpretability methods for operational transparency
- Support integration with perception and tracking subsystems
- Collaborate on model retraining and update strategies
- Use automated testing to validate model behavior
- Stay current with advancements in computer vision and deep learning
- Contribute to architectural decisions for ML infrastructure
- Work in agile development cycles with cross-functional teams
- Support rapid prototyping for new mission use cases
Nice to Have
- Master's or PhD in machine learning, computer vision, or related field
- Experience with real-time inference systems
- Background in defense, aerospace, or government technology
- Published research in machine learning or computer vision conferences
- Experience with multi-modal sensor fusion
- Knowledge of radar or infrared imaging systems
- Familiarity with embedded systems development
- Experience with reinforcement learning applications
- Contributions to open-source ML projects
- Experience with model compression and quantization
- Work with distributed training at scale
- Knowledge of regulatory or certification standards for deployed AI
- Experience in agile or scrum environments
- Leadership experience in technical projects
- Security clearance or ability to obtain one
Compensation
Competitive salary based on experience and qualifications
Work Arrangement
Hybrid with on-site requirements for integration and testing
Team
Collaborative engineering team focused on rapid development and deployment of autonomous systems for national security
Security Requirements
Applicants must be eligible to obtain and maintain a security clearance. U.S. citizenship is required for this position due to government contracting requirements.
Physical Demands
Occasional travel to test sites may be required. Role may involve working in outdoor environments and with hardware integration teams during field deployments.
