- 经验
- 任何
- 薪水
- —
- 职位空缺
- 1
- 发布
- 10小时前
- Work mode
- 在办公室
- Eligibility
- Applicants who have practical exposure to computer vision, deep learning, and related robotics or imaging workflows can apply.
- Resume
- Required to apply
Where you'll work
职位描述
About the company
BotsDeploy is building a robotics and AI platform aimed at helping businesses streamline and improve their operations. The team is looking for someone who wants to contribute to the future of robotics and intelligent automation.
Role overview
This position focuses on applying modern machine learning, deep learning, computer vision, and related methods to solve practical robotics use cases.
What you will work on
- Use machine learning, deep learning, and computer vision techniques to build intelligent solutions for robotics and business scenarios.
Required technical background
- Strong programming ability in Python or C++.
- Hands-on familiarity with at least one image or vision API such as OpenGL, OpenCV, or Open3D.
- Experience with at least one deep learning framework, such as PyTorch or TensorFlow.
- Exposure to an open-source annotation platform such as labelme, labelimg, or labelstudio, including the ability to define data collection rules, create annotation guidelines, and review data and label distributions statistically.
Preferred domain experience
- Image algorithms: knowledge of object detection, image segmentation, keypoint detection, or object tracking, along with common model structures, augmentation techniques, model training, evaluation, and architecture improvement.
- Stereo vision: understanding of depth cameras, multi-view geometry, and camera calibration, with exposure to depth estimation, 3D reconstruction, and point cloud handling.
- Image and video processing: familiarity with core image-processing concepts and deep learning methods such as denoising, low-light enhancement, stabilization, dehazing, and deblurring.
- Model acceleration: knowledge of compression methods like distillation, pruning, quantization, and fixed-point training, plus exposure to ONNX or TensorRT and deployment on Nvidia Jetson, ARM, or x86 systems.
Additional notes
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