Software Engineer, Video Processing

Veo; København; On-site; DKK 50,000 - 70,000; Full time

Job summary

Veo, located in Copenhagen, is seeking a skilled engineer to design and maintain video processing pipelines. This role involves real-time processing using sophisticated tools like GStreamer, CUDA, and Vulkan. You will work closely with cross-functional teams to ensure efficient integration of hardware and software.

The ideal candidate has experience with GPU programming, solid software engineering fundamentals, and is comfortable in Linux environments. This opportunity promises challenges that will allow you to impact the quality of every recording we deliver.

Qualifications

• Strong experience with GPU programming and compute shaders (Vulkan/CUDA).
• Knowledge of video processing and media pipelines, especially GStreamer.
• Solid fundamentals in software engineering: architecture, APIs, testing.
• Comfortable in Linux environments with build systems/tooling.

Responsibilities

• Design and maintain real-time video processing pipelines using GStreamer.
• Contribute to GPU-accelerated video processing algorithms.
• Improve video quality and performance metrics.
• Build testsuites for reproducible builds and debugging tools.
• Collaborate with other teams for seamless integration.
• Shape the architecture of the video processing stack.

Skills

GPU programming

Video processing

Computer vision

GStreamer

Linux

Rust

C++

Python

Tools

CUDA

Vulkan

Job description

Veo is a global leader in AI-based sports camera technology. Our innovative, fully automatic camera solution enables sports teams to record matches and training sessions without a camera operator. We’re democratizing the world of sports by granting video analysis for teams on all levels—a privilege that used to be only for the few. More than 40,000 clubs in 90+ countries record their games every week.

The Opportunity

Our cameras capture multiple high-resolution sensor streams in real time. The Video team builds the processing pipeline that turns those raw streams into the stable, stitched, analysis-ready video our customers rely on - and that our AI, coaching, highlights, and cloud products build on top of.

You’ll work on one of the most technically challenging parts of the Veo stack: real-time video processing across embedded devices and cloud environments, using GStreamer, in-house plugins, GPU acceleration, CUDA, and Vulkan compute shaders.

This is not a role far away from the product. It is right in the middle of the value chain, where camera hardware, firmware, video processing, AI, and customer experience meet. The work is deeply technical, highly visible, and directly connected to the quality of every recording Veo delivers.

What You Will Do

• Design, build, and maintain real-time video processing pipelines using GStreamer in embedded and cloud environments.
• Contribute to GPU-accelerated video processing algorithms (e.g., CUDA and Vulkan compute shaders).
• Improve video quality, robustness, latency, synchronization, keyframe handling, and failure recovery.
• Build CI-friendly testsuites, reproducible builds, and debugging tools that make complex media pipelines easier to develop and operate.
• Collaborate closely with firmware, camera, AI, platform, and cloud teams to ensure the video stack integrates cleanly and ships reliably.
• Help shape the long-term architecture of Veo’s video processing stack as our cameras, algorithms, and product ambitions evolve.

What You Could Bring

You do not need to match every item below. We are looking for strong engineers who enjoy difficult systems problems and want to learn deeply.

You’ll likely thrive in this role if you have experience with some of the following:

• Experience with GPU programming and compute shaders (Vulkan and/or NVIDIA/CUDA)
• Knowledge of video processing, computer vision and/or media pipelines, especially GStreamer (elements, caps negotiation, debugging, plugin development).
• Solid software engineering fundamentals: architecture, APIs, testing, observability, and maintainable systems.
• Proficiency in one or more of the languages we commonly use around the stack (depending on area): Rust / C++ / Python / Slang.
• Comfortable working in Linux environments and with build systems/tooling used in native code projects (e.g., Meson/Ninja-based workflows).

Environment

• Media stack: GStreamer + in-house GStreamer plugins
• Plugin development: includes Rust-based plugins and Rust/C/C++ interop patterns (bindgen/FFI)
• Build & packaging: Meson/Ninja workflows; Debian packaging and Docker-based dev/build flows
• GPU acceleration: CUDA and Vulkan
• Environments: Docker/Kubernetes, Embedded Linux (Yocto)