Machine Learning Engineer III – Computer Vision & Data Systems

About this role

At Zone 5 Technologies, we're redefining what's possible in unmanned aircraft systems. Our team of engineers and innovators is developing cutting-edge autonomous solutions that push the boundaries of UAS technology - solving complex challenges that matter.

We're building the future of UAS capabilities, and we're looking for exceptional talent to join us. If you're driven by hard problems, energized by rapid innovation, and ready to make an impact on next-generation flight systems, you belong here.

We are seeking a Machine Learning Engineer to build production-grade computer vision pipelines and manage end-to-end ML data infrastructure for autonomous aerial systems. You will own the full lifecycle from data curation through model deployment on edge hardware.

Responsibilities:

ML Model Development & Deployment

• Design, train, and optimize object detection and tracking models (YOLO, RT-DETR, ViT) for aerial platforms

• Deploy models on NVIDIA Jetson hardware using TensorRT, achieving real-time inference

• Integrate vision models into ROS2 autonomy stacks with efficient message passing and synchronized sensor data

• Profile and optimize model performance to meet compute, power, and latency constraints on embedded systems

Data Infrastructure & MLOps

• Build and maintain data pipelines for ingestion, labeling, versioning, and quality control of large-scale aerial imagery datasets

• Design active learning loops and data selection strategies to maximize model performance with minimal labeling effort

• Implement automated model evaluation frameworks and performance monitoring for deployed systems

• Create synthetic data generation pipelines and data augmentation strategies to improve model robustness

System Integration

• Develop ROS2 nodes for real-time sensor processing, model inference, and downstream autonomy integration

• Work with GStreamer and Jetson multimedia APIs for efficient camera pipeline management

• Collaborate ...