Machine Learning

2 weeks ago

Dhaka, Dhaka, Bangladesh Genius Farms Limited Full time $40,000 - $60,000 per year

Requirements

Additional Requirements
  • Required Qualifications
  • Strong interest in machine learning and computer vision.
  • Basic understanding of ML concepts (training, data, accuracy).
  • High attention to detail and consistency in visual tasks.
  • Familiarity with image annotation tools or a willingness to learn.
  • Currently pursuing or recently completed a degree in Computer Science, Data Science, AI/ML, or a related field.
  • Ability to work independently and follow structured processes.
Responsibilities & Context

Location: Dhaka

Duration: 2–3 Months (Extension possible based on performance)

Compensation: As per company policy

About the Role

We are looking for passionate and detail-oriented Machine Learning & Data Annotation Interns to join our Computer Vision team. This role is ideal for fresh graduates or early-career professionals eager to gain practical exposure in the machine learning field — particularly in object detection and dataset preparation.

Interns will work on annotating large-scale image datasets and also get involved in supporting the ML pipeline, including assisting with model evaluation, dataset curation, and basic experimentation workflows. This is a fantastic opportunity to build your foundational knowledge in ML while contributing directly to production-grade projects.

What You'll Learn

  • End-to-end lifecycle of training object detection models.
  • Importance of high-quality labeled data in computer vision systems.
  • Hands-on use of annotation tools and data management best practices.
  • Introduction to model training, evaluation, and real-world ML applications.
  • Collaboration in a real ML team environment with mentorship and feedback.

Key Responsibilities

  • Annotate images using tools like Labeling, CVAT, or Roboflow with attention to accuracy and consistency.
  • Collaborate with ML engineers to understand how annotations affect model training and accuracy.
  • Assist in dataset curation tasks — including image filtering, cleanup, and formatting.
  • Participate in model evaluation by reviewing predictions and identifying false detections or misclassifications.
  • Support dataset versioning and organization for multiple training iterations.
  • Suggest improvements to labeling guidelines and annotation workflows based on practical challenges.
  • Engage in internal learning sessions to understand object detection concepts and toolchains.

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