The RoleReporting 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 Technology StackOur 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
What you'll do- 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.
Collaborating withYou will work closely with applied science engineers, platform and infrastructure engineers, product managers, QA engineers and DevOps engineers, along with the operations teams that deploy our software and our hardware partners such as NVIDIA.
Profile and Skills- 3-5 years building real time or high performance software in production.
- Hands-on experience building real time applications for vision AI or video pipelines.
- Strong programming skills in a systems language. C/C++ and Rust are preferable, along with Python for tooling and glue.
- Experience with streaming and video frameworks such as FFmpeg, GStreamer or similar is a strong advantage.
- Experience in the NVIDIA ecosystem (CUDA, TensorRT, DeepStream, GPU programming) is a strong advantage.
- Solid understanding of concurrency, networking and performance optimization.
- Comfortable with Linux, Docker, Kubernetes, Git and CI/CD pipelines.
- In-depth knowledge of cloud security, scalability, and performance optimization principles.
- A reliability-first mindset. You build systems that hold up under real world load with efficiency and performance front and center.
- 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, Engineering or a related field, or equivalent practical experience.
