Senior Computer Vision / Applied AI Engineer, Simbe

• The right person isa strong ML engineer, an exceptional software engineer, and a practical builder who enjoys messy real world data, rapid iteration, and measurable customer impact
• 5+ years of experience in computer vision, applied machine learning, robotics perception, or related production AI systems
• Strong Python experience and hands on experience with PyTorch or TensorFlow
• Experience training and evaluating object detection, segmentation, OCR, visual search, or image recognition models
• Strong understanding of dataset curation, annotation quality, model evaluation, error analysis, and experimentation
• Experience building maintainable production code and data pipelines in a Linux based environment
• Ability to balance research quality with production constraints such as latency, compute, memory, robustness, and ease of deployment
• Strong communication skills and a customer value mindset

Desirable

• Simbe is looking for a Senior Computer Vision / Applied AI Engineer to build production AI systems that turn store imagery into trusted retail intelligence
• This role will work across dense product detection, price tag and promo tag detection, OCR, barcode decoding, product association, segmentation, visual search, model evaluation, and customer specific model improvements
• You will work on dense, cluttered, real world visual scenes where accuracy directly drives customer value
• You will help improve core Simbe use cases such as out of stock detection, price accuracy, promo compliance, top stock, and product location intelligence
• You will work on the full model lifecycle: data, annotation quality, training, evaluation, release, monitoring, and production debugging
• Build production CV models. Design, train, validate, and deploy models for object detection, segmentation, OCR, barcode localization, product recognition, shelf understanding, and other customer facing computer vision tasks
• Own dataset quality. Curate, clean, version, and analyze large real world training datasets, including hard negative mining, annotation QA, data audits, and active learning workflows
• Improve model performance. Research and implement model architecture, loss function, data augmentation, synthetic data, and evaluation improvements that increase precision, recall, latency, and robustness across customers
• Support model releases. Contribute to model validation, release gates, inference wrappers, ONNX/TensorRT exports, and production monitoring so models are reliable in customer environments
• Develop tooling. Build internal tools for model evaluation, annotation review, error mining, data visualization, dataset exports, and deployment readiness
• Partner cross functionally. Work closely with Product, Customer Success, Data, Robotics Software, and Field Operations to turn customer issues into model and pipeline improvements
• Stay current. Track modern work in open vocabulary detection, promptable segmentation, multimodal product understanding, visual search, OCR, and edge inference, and evaluate where it can create value for Simbe