Machine Learning Engineer
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 50,000 clubs in 90+ countries record their games every week.
Growing as fast as we do in Veo means that every day is different, exciting, and challenging, both on the front line and in the back office.
But that’s not the most remarkable thing about us.
The coolest thing is our people. We’ve attracted some of the brightest minds in the industry. They are the reason we can create a great product and do it while enjoying ourselves.
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 15 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 senior engineer/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
- 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
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