Junior Computer Vision Engineer

Experience: Mid-Senior level 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 Ege University, 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 team players who will contribute to deliver state-of-the-art solutions with us. We're looking for a Junior Computer Vision Engineer to join us! Key Responsibilities Oversee and execute high-quality data annotation and labeling processes for image classification, segmentation, and 3D point cloud datasets. Implement and refine techniques for 3D image blending and stitching to ensure seamless transitions in reconstructed environments. Assist in training computer vision models, monitoring performance metrics, and performing hyperparameter tuning under the guidance of senior staff. Support the implementation of core algorithms in camera calibration, feature extraction, and image processing. Conduct rigorous testing of software modules and APIs to ensure the stability and accuracy of vision-based solutions. Maintain clear documentation of data workflows, training experiments, and code implementations. Requirements Bachelor’s degree in Computer Science, Electrical Engineering, or a related field (Master’s preferred) with a focus on Computer Vision or Robotics. 0–2 years of experience (internships or academic projects included) involving image processing or machine learning. Foundational knowledge of OpenCV and image processing primitives. Strong proficiency in Python (for scripting, data prep, and ML frameworks) and imtermediate/advenced with C/C++ . Understanding of 3D Geometry and coordinate transformations (relevant for 3D blending and calibration). Experience with deep learning frameworks such as PyTorch or TensorFlow . Familiarity with data labeling tools (e.g., CVAT, LabelImg) and version control (Git). A detail-oriented approach to data quality and a strong desire to learn complex mathematical concepts like linear algebra and numerical optimization in a real-world context.