Senior Computer Vision / Applied AI Engineer

Simbe is building the AI powered operating system for physical retail.

Our autonomous robots and multimodal computer vision platform turn complex, constantly changing stores into accurate, actionable intelligence for leading retailers around the world.

Simbe combines robotics, computer vision, machine learning, data infrastructure, and customer focused product design to help retailers improve shelf availability, price and promo execution, inventory accuracy, and store team productivity.

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.

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.

The base salary offered is based on market location and may vary depending on individualized factors for job candidates, including job related knowledge, skills, experience, and other objective business considerations.

Subject to those same considerations, the total compensation package for this position may also include equity compensation, in addition to a full range of medical, financial, and other benefits.

Details of participation in these benefit plans will be provided if an employee receives an offer of employment.

• 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.