ML Tech Lead – Computer Vision & Shelf Recognition

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.

•     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.

•     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.).