Operate across two core areas: Product QA and Customer Implementation. Product Development QA:
Test new AI features prior to release to ensure accuracy and reliability.
Evaluate outputs from computer vision models and analytics pipelines.
Identify and document edge cases, failure modes, and inconsistencies.
Quantify performance using structured validation (e.g., accuracy checks, pattern analysis).
Partner with engineering to triage issues and drive iterative improvements.
Provide clear, actionable feedback to improve model performance and product quality. Customer Implementation & Validation:
Configure and deploy AI solutions for new customer locations.
Configure and deploy point-of-sale integrations.
Validate camera setups, configurations, and feature outputs for accuracy and readiness.
Perform end-to-end testing before customer handoff.
Identify and trace issues in live environments across hardware, video, data pipelines, and configurations.
Collaborate with engineering, product, and operations teams to resolve or independently fix issues.
Ensure a seamless transition to Customer Success with confident data quality.

High ownership; final owner of quality.
Technical background is a plus.
Strong systems thinking to understand interactions among hardware, software, and data pipelines.
Proven ability to identify root causes and resolve issues.
High attention to detail in validating data and video outputs.
Clear documentation and communication skills for both technical and non-technical teams.
Comfortable in a fast-paced, iterative environment.
Experience with computer vision systems (object detection, tracking, re-identification).
Familiarity with video analytics or AI-based products.
Experience with edge devices, cameras, or hardware deployments.
Experience analyzing structured data (CSV, JSON, SQL).
Exposure to QA processes, test planning, or validation frameworks.

QA Implementation Specialist – Vision AI Products

Key skills