14 hours ago
Melbourne, AustraliaStaff+
Responsibilities
- Research, design, and implement state-of-the-art perception capabilities from conception through field deployment.
- Deploy the AI stack to edge devices.
- Architect and develop tools that scale deep learning operations.
- Collaborate with deep learning engineers on research and development projects.
- Stay current with deep learning and computer vision literature.
Requirements
- 3–5 years of industry experience delivering deep-learning solutions for computer vision problems.
- Strong understanding of convolutional neural networks and/or transformers for classification, recognition, or segmentation.
- Experience working with recent foundation models.
- Experience implementing novel deep learning architectures using TensorFlow, Caffe, PyTorch, or similar frameworks.
- Bachelor’s, Master’s, or PhD in Computer Science or a related field.
- Preferred qualifications include publications in leading computer vision, artificial intelligence, or machine learning venues.
- Preferred experience includes C++ or Python, object detection, target tracking, SLAM, 3D reconstruction, camera calibration, behavior analysis, vision-language models, large multimodal models, and automated video surveillance.
- Preferred experience includes deploying and optimizing deep learning models in embedded production contexts, including pruning, quantization, and performance tuning.
- Preferred experience includes maintaining or setting up MLOps systems and mentoring junior engineers or researchers.
Categories
About Shield AI
Shield AI builds autonomous systems for military and national security customers, combining its Hivemind autonomy software with V-BAT and X-BAT unmanned aircraft and Aechelon simulation technologies. The privately held company sells hardware, software, and related services to U.S. and allied defense agencies. Founded in 2015 and headquartered in San Diego, it operates across the U.S., Europe, the Middle East, and Asia-Pacific, and its technology is used in operational deployments.