Computer Vision Engineer

2 days ago


bangalore, India Harvested Robotics Full time

At Harvested Robotics, we build AI-powered laser-weeding systems  for farms in India. We capture high-resolution RGB + NIR imagery , train custom models, and deploy them on rugged edge compute to identify weeds with speed, precision, and safety. We’re looking for a Computer Vision Engineer  who lives at the intersection of algorithms, data, and real-time edge deployment — someone obsessed with accuracy, latency, and pushing models into production on actual machines in the field. What you'll do Design, train, and optimise CV/ML algorithms for weed detection, crop segmentation, and health classification . Build and refine data pipelines : labeling, augmentation, dataset versioning, and quality control. Improve accuracy, robustness, and FPS  across varied farm conditions (lighting, soil, rains, dust). Deploy and optimise models on edge hardware  (Jetson, Qualcomm, x86, custom accelerators) with tight compute budgets. Implement classical CV + deep learning  (OpenCV, geometric transforms, segmentation/detection networks). Build and maintain real-time inference pipelines  using GStreamer, DeepStream , CUDA plugins, and custom kernels. Convert and optimise models using TensorRT, ONNX Runtime , quantisation (INT8/FP16), and graph optimisation. Work with the perception team on NIR/RGB fusion , extrinsic calibration, and sensor synchronisation. Analyse field failures and iterate on models fast . Build internal tools for evaluation, visualization, and automated benchmarking . What you need Strong grounding in computer vision, machine learning, and image processing . Experience with PyTorch/TensorFlow , ONNX, TensorRT, or similar optimisation stacks. Comfort with C++/Python , CUDA, and real-time edge deployments. Hands-on experience with GStreamer  and/or NVIDIA DeepStream  pipelines. Solid understanding of model evaluation, metric design, class imbalance, and dataset bias. Ability to work with large, messy, real-world datasets. Experience with embedded GPU optimisation, Jetson NX/Orin deployments, or Qualcomm accelerators. Familiarity with robotics perception, ROS, or sensor integration. Your models directly control when, where, and how our robot fires a laser at weeds . Precision and reliability define product success, safety, and farmer trust. This is not a lab role — your work runs on machines in actual farms every day.



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