Staff Deep Learning Engineer
Develop and deploy deep-learning perception solutions for autonomous and non-autonomous platforms, taking computer vision research into real-time edge production. Requires deep learning and computer vision experience, modern model architectures, and relevant tertiary qualifications.
About the job
Responsibilities
- Research, design, and implement state-of-the-art perception capabilities from concept through field deployment.
- Deploy AI and deep learning stacks to edge devices.
- Collaborate with deep learning engineers to architect and develop tools that scale deep learning operations.
- Monitor relevant research literature and contribute to R&D projects.
Requirements
- 3–5 years of industry experience delivering deep-learning solutions for computer vision.
- Strong understanding of convolutional neural networks and/or transformers for classification, recognition, or segmentation.
- Experience with recent foundation models.
- Experience implementing novel deep learning architectures using TensorFlow, Caffe, PyTorch, or similar frameworks.
- Bachelor's, master's, or PhD in Computer Science or a related field.
Nice to Have
- Publications in leading computer vision, artificial intelligence, or machine learning conferences or journals.
- C++ and/or Python development experience.
- In-depth knowledge of current deep learning architectures for computer vision and image processing.
- Experience with object detection, target tracking, SLAM, 3D reconstruction, camera calibration, behavior analysis, vision-language models, large multimodal models, automated video surveillance, or related fields.
- Experience deploying deep learning models in embedded production environments, including pruning, network quantization, and performance tuning.
- Experience setting up or maintaining MLOps systems and services.
- Experience mentoring junior engineers or researchers.
Skills
Deep Learning, Computer Vision, Convolutional Neural Networks, Transformers, Foundation Models, TensorFlow, Caffe, PyTorch, C++, Python, Object Detection, Slam, MLOps, Model Quantization, Network Pruning
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