Machine Learning Engineer

Veo is an AI-powered sports camera and analysis ecosystem built for one job: turning what happens on a pitch into proof. Capture, viewing, data and analytics, sharing- all in one system, all pointed at the same thing.

That's bigger than football. Every player, on every pitch, gets access to the kind of proof only the top academies used to keep for themselves. It's the future we're building.

Every youth match still has a moment nobody saw. A run that won the game, underneath everyone's radar. A kid written off before anyone actually checked. We exist to close that gap, one match at a time.

One camera on any pitch feeds an AI that tracks every player and the ball for the whole game, turning what happened into evidence instead of somebody's opinion.

That's the actual job here. Not shipping features. Building the system that gives proof to someone who used to rely on a hunch.

If you've ever watched a match and wondered who else saw what you saw, you already know why this matters.

We're looking for people who want to build that future with us.

As a Machine Learning Engineer at Veo you will get to work on challenging problems and directly contribute to the value created by our AI-driven end-user products.

You will become part of our AI team, which comprises 20+ researchers and engineers responsible for all stages in the machine learning lifecycles across different projects - from scoping and defining data annotation tasks to modeling, validation, and deployment. You will be free to determine the directions of the project you work on while also getting feedback and being encouraged to spar with the rest of the team to assist each other in improving and succeeding.

We stay current with the latest research and continuously discuss concepts and ideas in recent papers to assess their relevance to our tasks and product-specific challenges.

We are looking to add yet another ambitious, high-performing junior to mid-level engineer or/researcher with proven experience from real-world machine learning projects to the team. Experience with computer vision is advantageous but not a must.

What you will do:

• Train state-of-the-art machine learning models
• Write efficient inference pipelines for cloud and edge
• Define and supervise data annotation tasks
• Improve evaluation schemes to increase understanding of model performance and shortcomings
• Contribute to long-term planning and prioritization of tasks

What you will need:

• MSc or PhD in a relevant field
• Hands-on experience with real-world, large-scale machine-learning projects
• Great coding skills
• Skilled at both autonomous tasks and contributing to group efforts
• Ability to examine details closely and critically