The posting, in Shield AI's own words
archived Sep 8, 2026Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.
What you'll do:
Research, design and implement state-of-the-art perception capabilities, taking ideas from conception into world-class field solutions Work with and deploy our AI stack to edge devices Work in collaboration with the other deep learning engineers to architect and develop tools help to scale up our deep learning operations Stay abreast with the literature and actively involve in various R&D project(s)
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Required qualifications:
Demonstrable experience in delivering deep-learning-based solutions to solve computer vision problems with industry-based experience between 3 – 5 years Strong understanding of using convolutional neural networks and/or transformers for object classification, recognition or segmentation Experience working with recent Foundation Models Experience with implementing novel deep learning network architectures using existing frameworks (TensorFlow, Caffe, PyTorch or similar) Relevant tertiary qualifications (Bachelors/Master/PhD in Computer Science or related fields)
Preferred qualifications:
Publication(s) in world-leading Computer Vision/Artificial Intelligence/ conferences/journals (i.e., CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, PAMI, JMLR) C++ and/or Python development experience In-depth understanding of the latest network architectures for and image processing Experience with any of the following: object detection and target tracking, simultaneous localisation and mapping (SLAM), 3D reconstruction, camera calibration, behaviour analysis, foundation models, vision language models, large multi-modal models, automated video surveillance and related fields Experience deploying models in an embedded production context, including experience of structured and unstructured pruning, network quantization and performance tuning Experience in maintaining and/or setting up systems and services Experience in mentoring junior engineers/researchers in the related fields