Machine Learning Engineer

The Machine Learning Engineer will architect and deploy production AI/ML systems that power autonomous aerospace operations from edge to cloud. This role involves building and maintaining the complete ML lifecycle—spanning from data pipelines to deployed models—with a focus on computer vision for autonomous navigation, LLMs for mission planning, and agentic AI for enterprise optimization. The position requires taking full ownership of system reliability, performance, and the operational excellence necessary to enable next-generation autonomous Unmanned Aircraft Systems (UAS).

Essential Responsibilities:

ML Pipeline Development

• Build data processing and training pipelines using Google Cloud
• Develop computer vision models for obstacle detection, target recognition, and sensor fusion
• Implement automated retraining workflows and experiment tracking systems
• Create CI/CD pipelines for model deployment

Edge Deployment & Optimization

• Optimize models for edge devices through quantization, pruning, and compression
• Deploy inference pipelines on UAS hardware with power and memory constraints
• Implement visual-inertial odometry for GPS-denied navigation
• Develop over-the-air updates and offline-capable systems with network fallback
• Integrate sensor fusion algorithms (camera, LiDAR, radar, IMU)

Production Monitoring & Maintenance

• Monitor model performance, latency, and availability against SLAs
• Build drift detection, A/B testing, and alerting systems
• Troubleshoot production issues and maintain audit trails

LLM & Agentic AI

• Deploy and fine-tune LLMs for mission planning
• Build RAG systems and agentic AI workflows for enterprise operations

Qualifications & Experience:

Required:

• Bachelor’s degree in computer science, Applied Mathematics, or related technical field
• 5+ years building production ML systems; 3+ years with Google Cloud Platform
• Google Professional Machine Learning Engineer Certification (current or obtainable within 1 month)
• Expert Python; production experience with TensorFlow or PyTorch
• Computer vision systems: object detection, segmentation, tracking
• Edge deployment with TensorFlow Lite, TensorRT, or ONNX; model optimization experience
• MLOps: CI/CD, monitoring, experiment tracking
• Experience deploying LLMs and RAG systems
• U.S. Person status; ability to obtain DoD Secret clearance within 6 months

Strongly Preferred:

• Edge hardware deployment (NVIDIA Jetson, Google Coral TPU)
• Embedded Linux and ROS experience
• Defense/aerospace industry background
• Additional Google Cloud certifications
• Domain expertise: GPS-denied navigation, sensor fusion, UAV/drone systems, or adversarial ML

Physical Requirements:

• The ability to maintain a stable, upright seated or standing position for extended periods (typically 6–10 hours per day) without significant physical fatigue.
• High-frequency use of fingers and wrists for data entry, complex coding, and navigating digital interfaces using keyboards and mice.
• The capacity for "near-point" visual tasking, involving the ability to focus on high-resolution displays for long durations and process dense information without excessive eye strain.

Company DescriptionHeven AeroTech (Heven), Headquartered in Northern Virginia with offices in Washington, Florida and Israel, our team thrives on collaboration, diversity, and innovation. We are committed to creating a workplace where every voice matters, every effort is valued, and every team member has the tools to grow and succeed. We’d love to meet you if you’re ready to be the glue that holds a dynamic headquarters team together and lead with purpose.