Applied AI Engineer

The Role

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 Technology Stack

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

What you’ll do

• 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.

Collaborating With

You will work closely with the applied AI, platform and infrastructure engineers, MLOps engineers and product managers, along with the operations teams that deploy our software to customers. You will also work with our hardware and cloud partners such as NVIDIA and Google as new models move from the lab into stores.

Profile and Skills

• 3-5 years building and shipping machine learning or computer vision systems, with real time spent taking models into production rather than only doing research.
• Working knowledge of vision language models (VLMs) and world models, and a clear sense of where they help and where they do not.
• Strong programming skills in Python and hands-on experience with a modern deep learning framework such as PyTorch.
• Solid grounding in computer vision and deep learning fundamentals, including training, evaluation and debugging of real models on messy data.
• Deep understanding of computer vision and deep learning fundamentals including tuning, training, evaluation and debugging of real models on messy data.
• Experience building real-time vision AI applications is a strong advantage.
• Familiarity with model optimization and deployment tooling such as TensorRT, ONNX, CUDA and GPU inference.
• Comfortable working with large scale video or image data and the infrastructure around it.
• Solid understanding of software engineering principles, version control (Git), and CI/CD pipelines. Ability to integrate and maintain strict standards of code and model quality for easy long-term maintenance.
• A results-first mindset. You care whether the model works in the store, not just on the benchmark.
• Excellent communication skills coupled with the ability and desire to work independently in a fast moving, delivery focused team. You enjoy solving complex problems creatively.
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Engineering or a related field, or equivalent practical experience.