About SkillCorner
At SkillCorner, we turn a single broadcast camera feed into rich sports data, 100% automatically. Our Computer Vision and Machine Learning algorithms detect every player, the ball and the referees, locate them on the field, track them frame by frame and recognize who is who.
Since 2016, we have grown into a global leader in our field. We cover more than 150 men’s and women’s competitions, hold over 100 billion data points on more than 100,000 professional players, and are trusted by more than 350 football, basketball and American football teams. Clubs, federations and agencies use our data to rethink how they analyze performance, scout and recruit.
We are a team of about 150 people around the world, with our tech team based mostly in Paris. The Computer Vision team, 16 people strong, sits at the heart of our mission: it uses state-of-the-art deep learning to deliver precise, high-quality tracking data, at scale and in production.
The role
We are looking for a Senior Computer Vision Scientist to join our Basketball Tracking team, reporting to the Team Lead. You will bring deep expertise in deep learning to the models that power our basketball tracking data, from detection and tracking to re-identification, recognition and pose estimation.
You will own key models end to end: designing architectures, building training and evaluation pipelines, and pushing performance to production-grade quality. You will make architectural decisions with real downstream impact and help raise the research and engineering standards of the team.
This role sits where applied research meets production. Your work will go from experiment to live deployment, used by the professional basketball teams SkillCorner works closely with around the world.
What you’ll do
Deep learning research and development
Design, train and optimize state-of-the-art deep learning models (CNNs, Transformers, GNNs) for detection, tracking, re-identification, recognition and pose estimation
Own key components of the basketball tracking pipeline end to end, from data and training to evaluation and deployment
Make and defend architectural decisions, grounded in a solid understanding of the theory behind the models you build
Monitor the state of the art, analyze model performance and turn new ideas into concrete improvements
Establish the metrics and tooling that measure tracking quality, and drive continuous improvement from one release to the next
Engineering excellence
Bring research algorithms into our AWS production pipeline and keep performance stable when new models go live
Optimize models for accuracy, speed and cost at scale
Raise the bar on engineering and research practices: code reviews, testing, reproducible experiments, documentation and smart use of LLM-based tools
Investigate issues in the pipeline and keep code quality high
Understand client needs and product stakes, and translate them into concrete tracking improvements
Share your knowledge and support more junior team members as they grow
