Job Description
Staff Engineer, Deep Learning (R5180)
SHIELD AI - 2.0
Melbourne, FL Job Details 1 day ago Qualifications AI models AI platforms (beyond public GPTs) Computational framework Classification analysis Neural networks Machine learning frameworks Full Job Description Founded in 2015, Shield 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 and V-BAT and X-BAT aircraft. 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. Job Description:
Join Our Team:
Shape the Future of Perception Technology! Are you ready to revolutionize the world of perception capabilities for both autonomous and non-autonomous platforms? At the forefront of innovation, we are pushing the boundaries of what's possible, turning cutting-edge insights into real-time, deep-learning-based solutions to solve practical perception challenges on the edge. Your skills will play a key role in driving transformative solutions that redefine the future. Be part of a dynamic team where innovation meets impact. Let's shape the future together! 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) 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/Machine Learning conferences/journals (i.e., CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, PAMI, JMLR
) C++ and/or Python development experience In-depth understanding of the latest deep learning network architectures for computer vision 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 deep learning 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 MLOps systems and services Experience in mentoring junior engineers/researchers in the related fields #LI-FB1 #LD Help us redefine what's possible in AI-driven perception — apply today! Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, colour, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
