Customer Facing 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 Customer Facing Applied AI Engineer to help turn our computer vision and data platform into customer value across new and existing retail deployments. This role sits at the intersection of Computer Vision, Data, Product, Customer Success, and Field Operations. You will configure, tune, validate, and troubleshoot Simbe's AI pipelines for customer environments, helping retailers get accurate and actionable insights from Tally as quickly and reliably as possible.

Why This Role Is High Impact

• You will be close to the customer and close to the technology, with direct influence on customer outcomes.
• You will translate real world deployment issues into repeatable product, pipeline, and model improvements.
• You will help Simbe scale across diverse store formats, fixture types, product categories, and retailer operating models.

Responsibilities

• Lead customer specific AI configuration. Configure and tune Simbe's computer vision and data pipelines for new customers, new store formats, and expanded robot deployments.
• Improve customer accuracy. Investigate and resolve issues related to out of stocks, price tags, promo tags, top stock, product association, fixture detection, shelf coverage, and data quality.
• Support launches and expansions. Partner with Sales, Implementation, Product, Customer Success, Data, Computer Vision, and Field teams to ensure new deployments meet customer requirements and scale smoothly.
• Own release validation. Test customer facing model and pipeline updates, evaluate accuracy impacts, and help manage deployment readiness for releases tied to customer needs.
• Analyze production data. Use Python, pandas, SQL, internal tools, logs, robot imagery, and customer outputs to diagnose problems, quantify impact, and recommend fixes.
• Build operational tooling. Create or improve internal tools that reduce deployment time, automate QA, expose data quality issues, and help customer facing teams move faster.
• Close the loop with Product and Engineering. Turn repeated customer issues into clear product requirements, engineering tickets, and model improvement opportunities.