Computer Vision Data Scientist

Responsibilities

Design, develop, and optimize computer vision models for object detection, image & instance segmentation, multi-object tracking, and human pose estimation; Build and train deep learning models using frameworks such as PyTorch or TensorFlow; Convert and optimize models for edge deployment using TensorRT, OpenVINO, NVIDIA TAO Toolkit; Perform model quantization, pruning, benchmarking, and latency optimization; Deploy models on edge hardware platforms (NVIDIA Jetson, Intel-based edge devices, etc.); Write clean, scalable, and production-ready code in Python; Collaborate with cross-functional teams including ML engineers, software developers, and hardware teams; Conduct performance evaluation, debugging, and continuous improvement of deployed systems; Stay updated with the latest research and advancements in computer vision and edge AI

Requirements

Strong expertise in object detection architectures (e.g., YOLO, Faster R-CNN, SSD); Segmentation models (e.g., U-Net, Mask R-CNN); Tracking algorithms (e.g., DeepSORT, ByteTrack); Pose estimation frameworks (e.g., OpenPose, HRNet); Proven experience deploying optimized models using TensorRT, OpenVINO, NVIDIA TAO Toolkit; Strong Python programming skills; Experience with model optimization techniques (INT8 quantization, FP16, pruning); Familiarity with ONNX and model conversion pipelines; Understanding of GPU acceleration and edge hardware constraints