Senior Machine Learning Engineer

Company Description

We’re the world’s leading sports technology company, at the intersection between sports, media, and betting. More than 1,700 sports federations, media outlets, betting operators, and consumer platforms across 120 countries rely on our know-how and technology to boost their business.

Job Description

Join our division, “Sportradar ad:s”, where we provide data-driven marketing and sponsorship services to the iGaming industry. Our purpose-built tools and services cater to the individual marketing needs of sportsbooks and media publishers worldwide. Imagine working in an environment surrounded by sports data, live play-by-play statistics, virtual sports, microservices, and betting – all while maintaining an agile mindset and a commitment to delivering exceptional value for our customers. We’re passionate about our work and deeply care about our people. If you’re ready for the challenge, join us in growing, innovating, and developing!    

The AI team plays a crucial role in expanding and enhancing our product offerings with cutting-edge Machine Learning (ML) and Generative AI solutions. We leverage AI to unlock new opportunities for our clients, providing smarter, data-driven marketing strategies and automating complex decision-making processes. Additionally, we streamline internal operations by generating in-depth analytics, enabling our teams to work more efficiently and make better-informed decisions. 

THE CHALLENGE: 

As a (Senior) ML Engineer in the AI team at Sportradar, you will have the opportunity to shape and contribute to all components of our AI stack—from the initial idea to the final product. Your work will span model training and fine-tuning, MLOps, deployment and monitoring, ensuring our
AI-driven solutions are scalable, efficient, and impactful. 

We follow an experiment-based approach, continuously testing new ideas, analyzing results, and sharing insights across the division to drive innovation. You will collaborate closely with sales and product managers to develop cutting-edge AI solutions that enhance our marketing and sponsorship services. Our technology stack relies on scalable microservices hosted in the cloud, allowing us to build flexible and high-performance AI-powered products.  

From a technical perspective, our AI solutions are built on a scalable, cloud-native architecture within AWS. We leverage Infrastructure-as-Code (IaC) with Terraform to ensure consistency and efficiency across our environments.
Our backend is predominantly developed in Python, with AWS Lambda powering our serverless compute, Amazon S3 for data storage, and CloudWatch for monitoring and observability.
For AI/ML workloads, we rely on Amazon SageMaker and Bedrock, enabling seamless model training, fine-tuning, and deployment. Our analytics stack includes AWS Athena and QuickSight, providing powerful insights to drive data-driven decision-making. Continuous integration and deployment (CI/CD) are managed through GitLab CI, ensuring smooth and reliable software delivery. As an engineering team, we focus on building extensible, well-abstracted, and maintainable systems. Thoughtful refactoring and robust testing practices guide our development process, ensuring high-quality, scalable AI solutions. 

Key Responsibilities:  

• Contribute and grow as part of a self-organized, agile software development team   

• Build our advertising technologies - from understanding the requirements to creating the fully functional data/AI solutions 

• Design and craft highly scalable, fault-tolerant ML/AI systems, while keeping your focus on quality, observability and security  

• Engage in a supportive, collaborative team environment and contribute to maintaining a positive culture  

• Share ideas and insights to different stakeholders within the division 

ABOUT YOU:  

• Professional experience in Data Science, Machine Learning, or AI engineering, with a focus on Natural Language Processing (NLP), Speech Recognition, or Computer Vision. Experience with Generative AI is a strong plus. 

• Strong problem-solving skills and the ability to think critically when designing AI-driven solutions. 

• Hands-on experience with ML model training, fine-tuning, deployment, and monitoring. Experience with cloud-based solutions (AWS SageMaker and Bedrock) is a strong plus. 

• Proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or Hugging Face.