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.
As a Machine Learning Engineer in Sportradar's AI Unit, you will leverage Sportradar's unique sports datasets, betting transaction data, user interaction data, and large-scale tracking data to develop innovative AI and analytics solutions. Your work may focus on predictive analytics for sportsbook risk management and liquidity-driven odds trading, or on generative AI and deep learning solutions for sports performance modelling, simulation, and virtualization.
Responsibilities:
- Analyse, explore, and visualize large-scale datasets using statistical methods and modern data science tools.
- Develop machine learning models ranging from predictive and statistical models to deep learning and generative AI solutions.
- Design and implement data processing, training, validation, and inference pipelines for production-grade AI systems.
- Rigorously validate methods, models, algorithms, and hypotheses through historical back-testing, simulations, and experimentation.
- Bring AI solutions into production leveraging Sportradar's AI platform, ensuring model monitoring, maintenance, and continuous improvement.
- Design innovative approaches for analysing sports, betting, tracking, and behavioural data to enhance products and services.
- Collaborate with product and engineering teams to integrate model outputs into real-world product experiences.
- Present complex technical concepts and findings to both technical and non-technical stakeholders.
Requirements:
- Strong knowledge of machine learning, statistics, and data science.
- Proven software engineering experience and strong programming skills in Python and Java.
- Experience building and deploying production-grade ML solutions.
- Proficiency with modern ML frameworks such as PyTorch and large-scale data processing.
- Advanced SQL skills and experience working with large datasets.
- Experience in one or more of the following areas: Predictive modelling, risk management, trading, or quantitative analytics. Deep learning, Natural Language Processing, transformers, sequence modelling, or representation learning.
- Experience validating models through experimentation, simulations, and back-testing.
- Experience with cloud computing, preferably AWS.
- Experience with agentic coding tools, like Cursor or Claude code.
- Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.
- Fluent in English (written and spoken).
- Autonomous, analytical, creative, and collaborative mindset.
Benefits:
- A collaborative environment with colleagues from all over the world.
- Flexibility to manage your workday and tasks with autonomy.
- A balance of structure and autonomy to tackle your daily tasks.
- Vibrant and inclusive community, including Women in Tech and Pride groups which welcome all participants.
- Global Employee Assistance Programme.
- Calm and Reulay apps.
- Online training videos.
- Flexible working hours.