Applied AI Engineer

Reporting to the VP of Engineering who leads the development of our next generation platform, you will help build the next generation platform that powers Everseen’s products.

This is hands-on work at the core of what we ship.

You will design, train and take models into production that run against live retail video at massive scale.

You will work with the latest advances in vision and multimodal AI, including vision language models and world models, and the systems that make them run in real time.

This is a high impact role that will enable you to take on complex problems and see your work reach thousands of stores and impact customer journeys in real time.

This role will allow you to take your career to the next level, working alongside industry experts and scaling a successful global organization.

Our engineering teams at Everseen have the opportunity to work with and develop skills across a modern, high-performance tech stack:

• Languages: Python, C/C++, CUDA (for GPU-accelerated computing)
• AI/ML & Computer Vision: PyTorch, OpenCV, TensorRT, ONNX
• Tools & Infrastructure: Linux, Docker, Kubernetes, Git, and CI/CD pipelines
• Data & Streaming: Real-time video processing, RTSP/video streaming, and cloud platforms
• Design, train, evaluate and own vision and multimodal models for Everseen’s next generation platform, and take them from prototype through to production.
• Apply vision language models and world models to real retail problems, and understand what holds up at scale and what does not.
• Design and implement targeted solutions to optimize system efficiency, real-time performance, and output.
• Build and optimize real-time vision AI pipelines that process live video across thousands of checkouts and stores.
• Partner with research and platform engineers to move new model architectures into production under tight latency and cost constraints.
• Run experiments, measure results honestly, and let the data decide what ships.
• Optimize models for GPU and edge deployment using tools such as TensorRT and ONNX, trading off accuracy against speed and footprint.
• Contribute to and collaborate on the shared codebase, review other engineers’ work, and help raise the bar across the team.
• Keep up with new research in computer vision and multimodal AI, expand on experimental results presented by others and successfully transition from research to production.