Senior Staff Deep Learning Engineer
Develop and deploy advanced deep-learning computer vision and perception solutions for edge devices, while contributing to research, scalable MLOps tooling, and production optimization. Requires 3–5 years of industry experience, relevant tertiary qualifications, and expertise in modern vision architectures.
About the job
Responsibilities
- Research, design, and implement state-of-the-art perception capabilities, taking ideas from conception through field deployment.
- Develop real-time, deep-learning-based computer vision solutions for autonomous and non-autonomous platforms.
- Deploy AI and deep learning stacks to edge devices.
- Collaborate with deep learning engineers to architect and develop tools that scale deep learning operations.
- Stay current with research literature and participate in R&D projects.
- Mentor junior engineers and researchers.
Requirements
- 3–5 years of industry experience delivering deep-learning-based solutions for computer vision problems.
- Strong understanding of convolutional neural networks and/or transformers for object classification, recognition, or segmentation.
- Experience working with recent foundation models.
- Experience implementing novel deep learning network architectures using TensorFlow, Caffe, PyTorch, or similar frameworks.
- Bachelor's, master's, or PhD in computer science or a related field.
Nice-to-haves
- Publications in leading computer vision, artificial intelligence, or machine learning conferences or journals, such as CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, PAMI, or JMLR.
- C++ and/or Python development experience.
- Deep understanding of current deep learning architectures for computer vision and image processing.
- Experience with object detection, target tracking, SLAM, 3D reconstruction, camera calibration, behavior analysis, foundation models, vision-language models, large multimodal models, or automated video surveillance.
- Experience deploying deep learning models in embedded production environments, including pruning, network quantization, and performance tuning.
- Experience maintaining or setting up MLOps systems and services.
Skills
Deep Learning, Computer Vision, Convolutional Neural Networks, Transformers, Foundation Models, TensorFlow, Caffe, PyTorch, C++, Python, MLOps, Slam, Model Quantization, Model Pruning, Edge Deployment
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