Description
Join Shield AI's team to revolutionize perception capabilities for autonomous and non-autonomous platforms. As a Senior Staff Deep Learning Engineer, you will research, design, and implement state-of-the-art perception capabilities, turning cutting-edge insights into real-time, deep-learning-based solutions to solve practical perception challenges on the edge.
Responsibilities:
- Research, design, and implement state-of-the-art perception capabilities, taking ideas from conception into world-class field solutions
- Work with and deploy the AI stack to edge devices
- Collaborate with other deep learning engineers to architect and develop tools to scale up deep learning operations
- Stay updated with the literature and actively participate in various R&D projects
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