Software Engineer

Reporting to the VP of Engineering who leads the development of our next generation platform, you will build the real time systems that run Everseen’s vision AI in production.

This is the software layer that takes live video from thousands of stores, moves it through our vision pipelines and turns model output into action, all under tight latency and reliability constraints.

You will work on the next generation platform with a modern, high performance stack, and your code will run at a scale most engineers rarely get to touch.

It is a hands-on, high impact role for someone who likes building fast, dependable systems close to the hardware.

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: Rust, 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
• Build and maintain the real time software that powers Everseen’s vision AI pipelines, from video ingest through to model serving and downstream actions.
• Design low latency, high throughput services that process live video across thousands of checkouts and stores.
• Work with applied science and platform teams to turn models into production services that meet latency, cost and reliability targets. Design and implement reusable, testable, efficient, and elegant code based on requirements.
• Optimize performance across the stack, from video decoding and streaming through to GPU inference and memory use.
• Build for reliability by handling failure, backpressure and scale so the system keeps running when stores are busy.
• Contribute to shared libraries, tooling and standards, review code, and help keep the codebase healthy. Create, maintain, and run unit tests. Ensure documentation is created and maintained.
• Debug hard problems in production environments where video, hardware and networks all interact.
• Keep learning the stack, from streaming frameworks to GPU tooling, and bring good ideas back to the team.