Machine Learning Engineer

About the role

You will architect and implement the core machine learning pipelines that power our automatic sports camera systems, directly shaping how teams around the world capture and analyze their performance. In this role, you own the design, development, and deployment of models that process high-volume video streams in real-world conditions. You will collaborate closely with researchers and product managers to translate abstract AI concepts into scalable, production-grade software solutions. Your work will involve rigorous experimentation, where you iterate on model architectures and training strategies to solve complex problems in sports computer vision. You will be responsible for maintaining the integrity and performance of our machine learning lifecycle from initial data scoping to final deployment and monitoring. This position requires you to proactively identify bottlenecks in data annotation and model behavior, then drive improvements through data-driven decisions. You will own specific project directions while actively participating in team discussions to refine approaches and share knowledge. By staying engaged with the latest research, you will help evaluate new techniques and determine their practical application to our products and customer needs.

Key facts

What you'll do

Design and implement scalable machine learning pipelines that ingest, process, and prepare large-scale sports video data for training and inference.

Conduct rigorous experiments to evaluate model performance, iterating on architectures, loss functions, and training procedures to solve challenging computer vision tasks.

Collaborate with data annotators and domain experts to define and refine data annotation tasks, ensuring high-quality labeled datasets that drive model accuracy.

Perform detailed analysis of model failures, diagnosing root causes and implementing corrective actions to improve robustness and generalization.

Optimize models for deployment in production environments, focusing on inference speed, memory efficiency, and integration with existing backend systems.

Work closely with the AI team to scope new projects, define technical requirements, and establish clear milestones for model development and validation.

Stay current with advancements in machine learning research, critically reviewing recent papers and assessing their applicability to our specific product challenges.

Contribute to internal knowledge sharing by discussing findings, debating methodologies, and helping the team adopt best practices for model development.

Partner with cross-functional stakeholders to align technical solutions with business objectives and user needs across different sports and use cases.

Implement monitoring and evaluation frameworks to track model behavior in the wild, enabling continuous improvement and data-driven product decisions.

Lead the validation and testing of new models, establishing benchmarks and metrics that ensure reliable performance before wide-scale release.

Support the maintenance and evolution of deployed models, troubleshooting issues and retraining systems to adapt to new data and requirements.

Participate actively in code reviews and technical discussions, promoting clean, maintainable, and efficient implementations across the codebase.

Drive the end-to-end delivery of machine learning features, taking ownership of problems from initial ideation through to stable, user-facing deployment.

Requirements

You possess a Bachelor's, Master's, or PhD degree in Computer Science, Machine Learning, Data Science, or a closely related technical field.

You have hands-on experience with real-world machine learning projects, demonstrating your ability to move models from experimentation to production.

You are proficient in Python, with strong skills in libraries and frameworks commonly used for machine learning and data manipulation.

You have a solid understanding of computer vision concepts, including but not limited to object detection, tracking, and video analysis techniques.

You are experienced with deep learning frameworks such as PyTorch or TensorFlow, and you have built, trained, and deployed neural networks before.

You have a strong grasp of data preprocessing, feature engineering, and model evaluation methodologies to ensure high-quality results.

You are comfortable working in a fast-paced environment where priorities can shift, and you can manage multiple tasks while maintaining attention to detail.

You communicate clearly and effectively, both in writing and during collaborative discussions with technical and non-technical stakeholders.

Nice to have

Experience with deploying machine learning models in cloud or edge computing environments is a distinct advantage.

Familiarity with sports video analysis or related domains is beneficial but not required for success in this role.

Practical notes

This role is based in Copenhagen and is offered as a full-time position.

The position does not specify working hours, but given the full-time nature and global team collaboration, flexibility and availability during standard business hours are expected.

There is no mention of travel requirements, visa sponsorship, or application deadlines in the provided source material.