Introduction to the position
We’re seeking a hands-on ML Technical Lead to lead the development of our next-generation shelf recognition system using YOLOv8, synthetic data workflows, and potentially DINOv3-based architectures.
Your role
• Lead a small CV/ML engineering team and build the roadmap for detection and recognition models.• Design and optimize training pipelines for YOLO-based models (real + synthetic datasets).• Implement best practices for data collection, augmentation, annotation quality, and tiling for small objects.• Explore and evaluate approaches such as OBB vs. HBB, DINOv3 backbones, and multi-GPU distributed training.• Establish CI/CD workflows for model training, versioning, deployment, and A/B testing.• Mentor junior engineers and promote strong ML engineering culture.• Collaborate with product and operations teams to deploy models into retailer-facing applications.
Your qualifications
• 5+ years in computer vision/deep learning, including 2+ years in a lead role.• Strong experience with PyTorch, Ultralytics YOLO, and distributed training on AWS.• Expertise in synthetic data generation and annotation pipelines.• Experience with object detection on small objects, data imbalance, and augmentation strategies.• Excellent communication and cross-functional leadership.Bonus• Experience with DINO/DINOv2/v3, ViT-based backbones.• Background in retail tech, OCR, or dense shelf detection.• On-device optimization experience (TensorRT, TFLite, etc.).
Recruitment process
CV pre screening
AI Interview
Technical and fit interview with Zsystem team