CV/ML Engineer

Company Overview

(Company overview not specified.)

Job Summary

The CV/ML Engineer will focus on deploying computer vision models to edge devices, aiming to optimize performance for latency and accuracy. This role is crucial for establishing connectivity between servers and edge devices, containerizing applications, and enabling remote deployment. The engineer will be responsible for developing video processing pipelines and integrating these with TFG systems while performing necessary data aggregations.

Responsibilities

• Deploy computer vision models to edge devices with an emphasis on optimizing latency and accuracy.
• Establish and maintain connectivity between server and edge devices.
• Containerize applications to facilitate remote deployment.
• Develop and manage video processing pipelines integrated with TFG systems.
• Perform data aggregations as necessary.
• Utilize CNN architectures effectively in project implementations and ensure proper tuning of models.

Qualifications

• Educational Background: Bachelor’s degree in Computer Science, Data Science, or a related field.
• Experience: 5 to 7 years of relevant experience in computer vision and machine learning.
• Technical Skills:
  • Proficiency in TensorFlow and PyTorch for building CNN models.
  • Strong knowledge of YOLO model fine-tuning and retraining.
  • Practical experience with YOLO models suitable for edge devices (e.g., YOLOv5n, YOLOv8n).
  • Familiarity with other object detection models, such as EfficientDet-Lite0 and MobileNet-SSD.
  • Experience with YOLO model quantization for Raspberry Pi (TFlite models).
  • Knowledge of object detection, image labeling tools, and feature extraction from video.
  • Exposure to Databricks and AWS is a plus.

Preferred Skills

• Additional familiarity with various machine learning frameworks and tools.
• Experience in deploying models in cloud environments.

Experience

A minimum of 5 to 7 years of specialized experience in computer vision and machine learning roles.

Environment

(Work setting, location, and any relevant environmental conditions not specified.)

Salary

(Salary range not specified.)

Growth Opportunities

(Growth opportunities within the company not specified.)

Benefits

(Benefits offered by the company not specified.)