AI Computer Vision Engineer

About Us:

At AssistIQ we are dedicated to creating a more efficient and transparent healthcare supply chain by empowering providers to generate accurate data and insights on their supply usage. We're building an AI-driven software solution that provides seamless tracking, real-time visibility and actionable insights to healthcare systems, enabling waste reduction and better revenue capture, leading to better value of care and better outcomes for patients.

About the Role:

As a Computer Vision/AI Engineer, you will be responsible for designing and developing reliable, maintainable, and scalable CV systems. In this role, you'll apply computer vision and machine learning to real-world challenges, integrating classical CV techniques and deep learning into production-ready products.

We're excited by candidates who enjoy and are capable of working in a fast-growing entrepreneurial environment. Your ultimate goal is to ensure we build new functionality in a pragmatic, scalable and manageable way.

Success in this role means delivering end-to-end computer vision solutions that solve meaningful problems at scale. We are looking for an engineer who embraces modern AI tools as a core part of their development workflow. You should be someone who actively uses AI to accelerate software development, improve code quality, troubleshoot problems, automate repetitive work and increase your overall engineering leverage.

We would expect you to be knowledgeable, articulate, sensible, able to coordinate with other peers and senior executives, and collaborate and deliver results in a fast-paced environment where teamwork, professionalism, confidentiality, and communication are essential.

What you will do

• Collaborate with product managers to translate real-world challenges into practical CV/ML solutions that create a measurable impact
• Design and develop computer vision and vision-language solutions using both classical techniques and modern AI approaches
• Optimize, deploy, and support models across cloud and edge environments, ensuring efficiency and robustness
• Establish evaluation frameworks for CV and VLM models, including real-world performance metrics
• Monitor deployed systems, debug issues, and refine models with new data or improved architectures
• Stay up to date with emerging research in computer vision; apply new techniques to improve vision products