Computer Vision and Deep Learning Assistant Engineer (Part-Time)

Rapsodo is a sports technology company that designs computer vision and machine learning products to help athletes maximize their performance. With offices strategically located in Singapore, Turkey, the USA, Japan, and the UK. Rapsodo is the undisputed leader in sports technology. Current partners include all 30 MLB teams, MLB, USA Baseball, Golf Digest, PGA of America, and over 1000 NCAA athletic departments.

Opened in 2018, our Turkey office operates as the R&D arm of Rapsodo. We have offices located in Bayraklı & Technopark in the Izmir Institute of Technology, recognized by the Ministry of Science, Industry and Technology as one of Turkey's most successful Technoparks. Our offices incorporate UI/UX, Mobile, Cloud Technologies along with Computer Vision, Deep Learning, Data Science and Unity teams. Rapsodo is rapidly growing, and we are looking for an intern or part-time contributor to work on Computer Vision and Algorithm Development tasks, with a focus on deep learning. The role involves: By constantly focusing on the user, you will have the opportunity to define and shape our bleeding edge and accessible sports technology products, helping athletes and aspiring sports players fulfil their potential.

Requirements

Requirements:

• Programming skills in Python
• Experience with deep learning frameworks (PyTorch)
• Understanding of image processing/computer vision techniques and handling large datasets
• Familiarity with Unity is nice to have
• Currently enrolled in 3rd or 4th year of a university.
  
  
  

Key Responsibilities:

• Develop and optimize workflows to handle large-scale satellite imagery efficiently
• Design & implement deep network architectures for golf course generation (enhancing satellite image and heightmap resolution and quality, texture generation/selection etc)
• Improve the efficiency of the 3D golf course modelling pipeline within Unity
• Collaborate with the team to identify innovative solutions for improving the accuracy and efficiency of the golf course generation process