Computer Vision Engineer

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

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.