Mobile Application Developer

BridgeView; Golden (CO); In loco; USD 90.000 - 130.000; Tempo pieno

Descrizione del lavoro

BridgeView envisions a Mobile Application Developer to craft a prototype that uses on-device AI to identify and count warehouse inventory. The role blends model training, mobile development, and edge AI deployment, with hands‑on testing occurring onsite.

You will train object detection models with YOLO variants, optimize for offline inference, and build a UI showing real‑time counts and overlays from camera feeds.

Competenze

• Strong Python programming skills for machine learning training pipelines.
• Experience with OpenCV, PyTorch or TensorFlow.
• Demonstrated ability to deploy ML models to mobile or edge devices.
• Proficiency with at least one mobile development framework, including Flutter, React Native, Swift, or Kotlin.
• Familiarity with image annotation, data augmentation, model optimization, and inference performance.
• Knowledge of hardware acceleration technologies, including Apple Neural Engine or GPU acceleration, preferred.

Mansioni

• Develop a mobile application prototype capable of counting stacked warehouse supplies using on-device AI.
• Train and refine custom object detection models using YOLOv8, YOLOv11, YOLO‑NAS, YOLOX, or similar frameworks.
• Establish an image annotation workflow using Roboflow, CVAT, or similar tools to label approximately 300–500 inventory images.
• Optimize and deploy trained models using CoreML or TensorFlow Lite for offline mobile inference.
• Build a mobile UI displaying real‑time object counts, bounding boxes, and detection overlays from camera feeds or uploaded images.
• Test and improve model accuracy across varying lighting conditions, camera angles, and warehouse environments.
• Collaborate directly with project stakeholders to iterate on functionality and troubleshoot deployment issues.

Conoscenze

Python

Computer vision

OpenCV

PyTorch

TensorFlow

Mobile development

Flutter

React Native

Swift

Kotlin

Image annotation

Data augmentation

Model optimization

Inference performance

Apple Neural Engine

GPU acceleration

Descrizione del lavoro

The Mobile Application Developer will develop a functional mobile application prototype that uses on-device artificial intelligence to identify and count stacked warehouse inventory. The role combines custom object detection model training, mobile application development, and edge AI deployment. Candidates must be available for frequent onsite collaboration and hands‑on testing.

RESPONSIBILITIES

• Develop a mobile application prototype capable of counting stacked warehouse supplies using on-device AI.
• Train and refine custom object detection models using YOLOv8, YOLOv11, YOLO‑NAS, YOLOX, or similar frameworks.
• Establish an image annotation workflow using Roboflow, CVAT, or similar tools to label approximately 300–500 inventory images.
• Optimize and deploy trained models using CoreML or TensorFlow Lite for offline mobile inference.
• Build a mobile UI displaying real‑time object counts, bounding boxes, and detection overlays from camera feeds or uploaded images.
• Test and improve model accuracy across varying lighting conditions, camera angles, and warehouse environments.
• Collaborate directly with project stakeholders to iterate on functionality and troubleshoot deployment issues.

EXPERIENCE REQUIRED

• Strong Python programming skills, particularly for machine learning training pipelines.
• Experience with computer vision frameworks such as OpenCV, PyTorch, or TensorFlow.
• Demonstrated ability to deploy machine learning models to mobile or edge devices.
• Proficiency with at least one mobile development framework, including Flutter, React Native, Swift, or Kotlin.
• Familiarity with image annotation, data augmentation, model optimization, and inference performance.
• Knowledge of hardware acceleration technologies, including Apple Neural Engine or GPU acceleration, preferred.
• Ability to work independently, research technical challenges, and resolve deployment issues.

PREFERRED EXPERIENCE

• Experience with YOLO-based object detection architecture is a plus.