Computer Vision Engineer

Harvested Robotics

0 years

0 Lacs

hyderabad telangana india

Posted:1 day ago| Platform:

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Skills Required

vision robotics ai capture algorithms data deployment latency design ml segmentation labeling dataset versioning lighting opencv inference cuda plugins onnx calibration evaluation visualization learning pytorch model nx ros reliability

Work Mode

On-site

Job Type

Full Time

Job Description

AI-powered laser-weeding systems

Computer Vision Engineer

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.

when, where, and how our robot fires a laser at weeds

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