About the Role
We are seeking an enthusiastic Computer Vision Engineer to join our team. This role is ideal for individuals eager to build a career in computer vision and AI for real-world automation, contributing to the development and deployment of cutting-edge CV models.
Responsibilities
- Assist in building and testing CV models for detection, tracking, classification tasks, and the latest foundation and VLM models.
- Prepare and annotate image/video datasets, supporting data ingestion and cleaning pipelines.
- Contribute to writing and debugging training scripts, model loaders, and preprocessing functions.
- Run evaluation jobs and generate performance reports using tools like TensorBoard or custom scripts.
- Support error analysis by identifying model weaknesses across edge cases.
- Collaborate with senior engineers on integrating models into scalable inference pipelines.
- Help visualize model outputs, draw bounding boxes, heatmaps, or segmentation masks for explainability.
- Document experiments and code for reproducibility and knowledge sharing.
- Utilize OpenCV, MediaPipe, and scikit-image for preprocessing, motion analysis, and visual overlays.
- Integrate DL models with post-processing logic (e.g., NMS, temporal smoothing, event triggering).
- Ensure low-latency inference by profiling and tuning frame-wise preprocessing.
- Support integration of RTSP video feeds and video decoders in test pipelines.
- Engage in point-cloud ingestion and processing (Open3D/PCL), calibration/registration (ICP/FGR), 3D detection/segmentation with sparse CNNs/PointNet, and RGB–LiDAR–IMU fusion.
Requirements
- Bachelor’s degree in Computer Science, Data Science, or a related technical field.
- 1–3 years of experience or strong internship/projects in computer vision or ML model development.
- Good Python skills and working knowledge of PyTorch or TensorFlow.
- Familiarity with image processing libraries (e.g., OpenCV, PIL) and dataset tools (e.g., COCO format, YOLO datasets).
- Exposure to object detection/tracking projects (academic, hackathons, or prior work).
- Basic understanding of synthetic data or 3D asset usage in training pipelines.
- Familiarity with Git, Linux command line, and Jupyter Notebooks.
- Eagerness to learn, take feedback, and contribute in collaborative development environments.