Deep Learning Engineer - Computer Vision for Sports Tech

Rapsodo; Çankaya; On-site; TRY 350,000 - 600,000; Full time

Job summary

Rapsodo is seeking a Deep Learning Engineer for its Computer Vision & Machine Learning team in Turkey. You will own the full model lifecycle from data collection to deployment, and you will design data pipelines, train and optimize models, and develop synthetic datasets using Blender and Python-based rendering.

You will work on deploying models to embedded hardware, defining evaluation benchmarks, and ensuring quality with automated tests while collaborating with cross-functional teams to

Qualifications

• Bachelor's degree in a related field (CS/CE/EE)
• Strong Python skills with PyTorch or TensorFlow
• Solid understanding of CV fundamentals and image formation
• Experience with Blender or 3D rendering tools
• Familiarity with Linux, Git, and Docker
• Strong analytical and problem-solving skills with data quality
• Good communication skills in English

Responsibilities

• Own the full deep learning model lifecycle from data collection to deployment
• Design and improve data collection, labeling, and annotation pipelines
• Train, evaluate, and optimize detection, segmentation, keypoint, and tracking models
• Develop synthetic datasets using Blender and Python-based rendering
• Build and maintain reproducible MLOps pipelines with experiment tracking
• Optimize models for deployment on embedded hardware
• Define evaluation benchmarks and ensure model quality through automated testing
• Collaborate with cross-functional teams to deliver production-ready AI solutions

Skills

Python

PyTorch/TensorFlow

Computer Vision

English communication

Education

Bachelor's degree in CS/CE/EE

Tools

Blender

Docker

Linux

Git

Job description

Rapsodo is seeking a Deep Learning Engineer for its Computer Vision & Machine Learning team in Turkey. You will own the full model lifecycle from data collection to deployment, and you will design data pipelines, train and optimize models, and develop synthetic datasets using Blender and Python-based rendering.

You will work on deploying models to embedded hardware, defining evaluation benchmarks, and ensuring quality with automated tests while collaborating with cross-functional teams to