Staff Deep Learning Engineer (R5677)

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! 

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) 

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) 

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