Machine Learning Engineer

We are looking for a Machine Learning Engineer to join Sportradar's AI unit, an established, high performing team of AI experts working on the design, development and deployment of AI models that deepen our understanding of sports and enable new sports technology products for sports performance, betting and media. The position will focus on the research and development of generative models of sports gameplay and predictive models of player actions and performance. You will collaborate closely with product owners and technical leads to ensure these models translate into real product impact across diverse areas such as coaching, sports betting, augmented streaming and sports virtualization. The successful candidate will be knowledgeable in modern machine learning techniques with strong fundamentals in deep learning for sequence modeling and generative AI. Strong programming skills and software development experience to build reliable training pipelines, scalable data processing, and production-grade inference services are a must. Hands-on experience working with spatiotemporal data and building models that learn from complex structured inputs is highly valued. Proficiency with Python and common ML tooling, like PyTorch and PyTorch Lightning, is required. You will also need a solid understanding of data engineering basics and experience working with cloud services. The role requires a Bachelor of Science in Computer Science, Engineering, Mathematics, Statistics, or a related field, with 5+ years of hands-on experience in engineering, including 3+ years in a machine learning/data science role. Fluency in English is essential, and the ability to work autonomously while being a team player is crucial.

Bachelor of Science in Computer Science / Engineering, Mathematics / Statistics, or related field; equivalent experience acceptable. 5+ years of hands-on experience in engineering, with 3+ years in a machine learning / data science role. Strong programming skills and software development experience. Hands-on experience with spatiotemporal data. Proficiency with Python and common ML tooling, like PyTorch and PyTorch Lightning. Solid understanding of data engineering basics. Experience working with cloud services (AWS). Fluent in English (written and spoken).