Edge ML Engineer — Maritime AI & Sensor Fusion

Quartermaster AI; Virginia (MN); On-site; USD 120,000 - 190,000; Full time

Job summary

Quartermaster AI in the United States is seeking an Applied ML Engineer to contribute across ML and perception tasks powering edge intelligent maritime systems. You will design, train, and optimize models for object detection, anomaly detection, and sensor-based inference on embedded hardware.

This role requires building lightweight pipelines, experimenting across CV, signal processing, and multi-modal fusion, and deploying real-world AI solutions under field conditions, with opportunities to

Qualifications

• Advanced degree in a relevant field or equivalent experience.
• 4+ years of production ML experience.
• Proficiency in Python and DL frameworks (PyTorch, TensorFlow).
• Experience with images, time-series, geospatial data, RF, etc.

Responsibilities

• Design, train, and evaluate models for object detection, classification, anomaly detection, and sensor-based inference.
• Optimize model architectures and inference pipelines for embedded/edge hardware.
• Contribute to dataset development, augmentation, and domain adaptation.
• Prototype across CV, signal processing, and multi-modal fusion.
• Implement real-time pipelines for on-device and cloud processing.
• Develop benchmarking, visualization, and debugging tools for ML models.
• Stay current with ML research and evaluate applicability to product roadmap.
• Participate in code reviews and documentation.
• Must be eligible to obtain/maintain a security clearance.

Skills

Python

Deep learning

Vehicle-edge computing

Education

Master's or PhD in Computer Vision, Machine Learning, Robotics

Bachelor's degree considered on a case by case basis

Tools

PyTorch

TensorFlow

Edge ML deployment

Job description

Quartermaster AI in the United States is seeking an Applied ML Engineer to contribute across ML and perception tasks powering edge intelligent maritime systems. You will design, train, and optimize models for object detection, anomaly detection, and sensor-based inference on embedded hardware.

This role requires building lightweight pipelines, experimenting across CV, signal processing, and multi-modal fusion, and deploying real-world AI solutions under field conditions, with opportunities to