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