Computer Vision Engineer

Computer Vision Engineer for Automated Content 

Interested in Artificial Intelligence, Computer Vision, Deep Learning and other modern technologies? Then the Automated Content team within Sportradar is the right fit for you. We are enhancing the sports experience for fans, athletes, and teams across the globe through world-class AI solutions and are committed to advancing the state of the art of Sport through continuous innovation with a specialized and distributed team focused on research and development in areas such as computer vision, machine learning, deep learning, data science and beyond. We are on a mission to automate sports content creation - the data and video – that feeds products and services we deliver to our partners. The possibilities to revolutionize the world of sport are vast, and we are only at the beginning of our journey. 

Our cutting-edge technologies will revolutionize the way individuals interact with and participate in sports. Our objective is to create innovative hardware and software solutions leading to automated viewing experiences, real-time statistics, and event forecasting. By doing so, we are enhancing the lives of coaches, athletes, and fans by developing products that cater to their evolving needs and requirements, as well as enabling an experience where people can engage with sport in new and meaningful ways. 

We seek a talented Computer Vision Engineer to join our team: a diverse, highly skilled, and enthusiastic team on the mission to reinvent the world of sports. 

About the role 

We are looking for a Computer Vision Engineer to reinforce our talented and enthusiastic Computer Vision (CV) Team, to obaining deep-data from videos of indoor sports. This dynamic team, composed of both remote and headquarters-based experts, works on challenging subjects such as player and ball tracking, segmentation, re-identification, 3D localization, event detection, etc. 

Your role will be to research, design, implement, compare, integrate and optimize CV solutions to leverages the power of sports data for millions of sports enthusiasts. 

Join the #1 sport’s data company to tackle real-world challenges, and help us change the way sport is consumed. 

Responsibilities 

• Research, design, implement, compare, integrate and optimize state-of-the-art CV/ML algorithms for our existing and upcoming products 

• Use CV/ML to automate and simplify the day-to-day work of manual operators 

• Share knowledge within the team located across multiple (European) countries 

The challenge 

• Stay up-to-date and apply state-of-the-art CV methods 

• Find an optimal solution in term of accuracy, considering real-time constraints 

• Collaborating with a talented team composed of both remote and headquarters-based experts 

• Integrate research into real-world products 

• Write clean, scalable, optimized, documented, and understandable code 

• Participate in code review processes, when area of expertise is applicable 

• Build reusable code libraries, when those are required 

Your skills 

• At least 5 years of professional experience with Computer Vision and Machine Learning (e.g. multi-objects tracking, segmentation, event detection, 3D estimation, etc.) 

• 3+ year experience in developing or using deep learning frameworks (e.g., PyTorch , TensorFlow, etc.) 

• Expert programming skills with Python (and its underlying data-science libraries) 

• Experience in AWS environment 

• Excellent communication skills in English and ability to effectively communicate complex technical concepts. 

• Must be comfortable with modern software development methodologies such as agile development, git, code review, etc. 

• Autonomous and rigorous, and a team player, with a positive mindset 

• Strong organizational and problem-solving skills 

• Be creative and innovation focused 

Sportradar is an Equal Opportunity Employer. We are committed to encourage diversity within our teams. All qualified applicants will receive consideration without regard to among other things, your background, status, or personal preferences