Staff Deep Learning Engineer (R5677)

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, 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)

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

Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check.

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.