About us: Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with the organization of the first Data Science UA conference, setting the foundation for our growth. Over the past 9 years, we have diligently fostered the largest Data Science Community in Eastern Europe.
About the project: This is an exciting project with a global leader in the travel industry, a company that is actively investing in cutting-edge technologies to drive a data-led transformation. As part of this initiative, the company has built a state-of-the-art data platform and is now expanding its capabilities to unlock new business insights.
About the role: We are looking for a Computer Vision Engineer to strengthen the team. The company is on a mission to make the world’s industrial environments safer with technology solutions, and to drive a paradigm shift in safety from reactive to proactive approaches to reduce the risk of industrial accidents. The team in Ukraine is developing the future of industrial safety based on machine learning, computer vision, and IoT.
This role focuses on research-driven development of computer vision models. You will generate and validate hypotheses, run controlled experiments, and explore new approaches to improve model performance across tasks like detection, segmentation, and pose estimation.
Requirements: — Bachelor’s or Master’s Degree in Computer Science or related field; — Solid grasp of machine learning and deep learning principles, with a strong theoretical foundation; — Proven R&D mindset: capable of formulating and validating hypotheses independently, exploring novel approaches, and diving deep into model failures; — Strong experience in Computer Vision, including object detection, segmentation, tracking, keypoint/pose estimation; — Proficiency in Python and deep learning frameworks such as PyTorch; — Practical experience with state-of-the-art models, including YOLO (v5/v8) and Transformer-based architectures (e.g., ViT, DETR, SAM); — Strong expertise in image and video processing using OpenCV; — Strong understanding of model training, evaluation, and optimization.
Would be a plus: — Experience with real-time video pipelines using FFmpeg and/or GStreamer; — Experience with model compression and acceleration techniques: quantization, knowledge distillation, or TensorRT; — Familiarity with AWS infrastructure, containerization (Docker), and MLOps best practices for reproducibility and deployment.
