Computer Vision Engineer

NXTGEN Industries; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Flexible/remote work

Office options in Melbourne and Sydney

Periodic travel to sites

Job summary

NXTGEN Industries is seeking a computer vision engineer to build and maintain models that detect defects in industrial inspection imagery. You will train, validate, and optimize object detection and segmentation models for edge devices, while ensuring data quality, versioning, and robust performance across environments.

The role spans data pipelines, model packaging, and continual evaluation to guide line operators on inspection decisions.

Qualifications

• 3+ years of production computer vision engineering experience.
• Strong Python skills and PyTorch or equivalent DL framework.
• Experience with object detection and segmentation architectures (YOLO/Detectron2).
• Knowledge of domain adaptation, fine-tuning, transfer learning for industrial data.
• Experience deploying models to edge devices using TensorRT or ONNX.
• Familiarity with active learning, annotation tools, and data quality practices.

Responsibilities

• Train and validate object detection and classification models for defects.
• Maintain data pipelines for annotation, dataset versioning, and augmentation.
• Address domain shift and class imbalance in industrial datasets.
• Optimize and package models for constrained edge hardware.
• Collaborate with robotics engineers on imaging settings affecting performance.
• Create evaluation frameworks and regression checks across deployment environments.

Skills

Python

PyTorch

Edge deployment

Object detection

Domain adaptation

Active learning

Data quality

Tools

TensorRT

ONNX

Detectron2

YOLO

Job description

Role overview

Build and maintain computer vision models that detect and classify defects in industrial inspection imagery. The work spans model training, data quality, edge deployment, and evaluation, with results used to guide what operators inspect and act on.

Responsibilities

• Train and validate object detection and classification models for structural, surface, and component defects.
• Maintain data pipelines for annotation, dataset versioning, and augmentation.
• Address domain shift and class imbalance in limited industrial inspection datasets.
• Optimise and package models for constrained edge hardware.
• Work with robotics engineers on image capture settings that affect model performance.
• Create evaluation frameworks and regression checks across deployment environments.

Requirements

• At least 3 years of production computer vision engineering experience.
• Strong Python skills and proficiency with PyTorch or a comparable deep learning framework.
• Experience with object detection and segmentation architectures such as YOLO-family models or Detectron2.
• Knowledge of domain adaptation, fine-tuning, and transfer learning for industrial data.
• Experience deploying models to edge devices using tools such as TensorRT or ONNX.
• Familiarity with active learning, annotation tools, and data quality practices.

Benefits and work setup

• Flexible or remote work is available within Australia, with office options in Melbourne and Sydney.
• Periodic travel to infrastructure, rail, and construction sites is part of the role.