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Full Stack Developer Intern Unpaid

Find Me
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Full Stack Developer (Machine Learning & Image Processing) Intern – Unpaid

About Us:

Findme is an innovative startup focused on creating cutting-edge applications. We are currently in the designing phase and gearing up to launch our product soon. Our team is passionate about technology and driven to make a difference in the IT field. Located in the United States, we offer remote work flexibility to our team members.

 

Desired Qualifications:

We are seeking a talented full-stack developer with the following skills and qualifications:

  • 3+ years of experience in Python, with a focus on image processing and machine learning.
  • Expertise in image recognition and computer vision libraries such as OpenCV, TensorFlow, PyTorch, or Keras.
  • Experience working with machine learning models for object detection and localization (e.g., YOLO, Faster R-CNN, or similar frameworks).
  • Familiarity with product detection in real-world environments, including handling occlusions, lighting variations, and noise.
  • Knowledge of augmented reality (AR) frameworks and their integration with machine learning models is a plus.
  • Strong proficiency in deploying models in production environments (experience with TensorFlow Lite, ONNX, or similar for mobile applications is highly desirable).
  • Experience with databases (SQL and NoSQL) for storing product and store data.
  • Familiarity with cloud platforms (AWS, Google Cloud, Azure) for training and deploying models at scale.
  • Excellent problem-solving skills, with a startup mindset and the ability to work independently in a fast-paced environment.
  • Strong communication skills, especially when collaborating with cross-functional teams, including non-technical stakeholders.

 

Responsibilities:

As a full-stack developer at Findme, you will:

  • Develop and fine-tune machine learning models for product recognition and in-store location identification using images.
  • Build and deploy image processing solutions that recognize products in real time through smartphone cameras.
  • Work closely with the AR development team to integrate AR-based product navigation.
  • Handle the pre-processing of image data, including augmentation, filtering, and feature extraction, to improve model accuracy and efficiency.
  • Optimize models for performance on mobile devices, ensuring fast and seamless product detection for users.
  • Set up systems to manage and update a database of products and store layouts, ensuring accurate product-location mapping.
  • Collaborate with data scientists and software engineers to ensure scalable deployment of the image recognition system.
  • Continuously test and refine the model to improve accuracy, speed, and user experience as more stores and products are added to the platform.
  • Help establish best practices for image processing and model deployment across the organization.

 

Skillset required:

  • Python (3+ years) - Image processing and machine learning.
  • Image recognition and computer vision (OpenCV, TensorFlow, PyTorch, Keras).
  • Machine learning models (YOLO, Faster R-CNN).
  • Product detection (handling occlusions, lighting variations, noise).
  • Augmented Reality (AR) frameworks (preferred).
  • Model deployment (TensorFlow Lite, ONNX).
  • SQL and NoSQL databases.
  • Cloud platforms (AWS, Google Cloud, Azure).
  • Mobile optimization.
  • Image data preprocessing (augmentation, filtering, feature extraction).
  • Strong communication skills.
  • Problem-solving and startup mindset.

 

Benefits:

  • Gain hands-on experience in product management within a startup environment.
  • Opportunity to earn academic credits.
  • Chance to convert this internship into a full-time role based on performance.
  • Work with a dynamic and innovative team in a fast-growing startup.

 

How to Apply:

To apply, please send your resume, portfolio, and a brief cover letter explaining why you’re excited about this opportunity to hr@fyndme.net. We look forward to seeing your work and how you can contribute to the future of retail technology.

 

Note: This is an unpaid internship with the possibility of academic credits and a potential full-time position based on performance.