Staff Engineer, Deep Learning (R5180)

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