Machine Learning Engineer

We are on a mission to pioneer the world’s next era of play. As we grow across Europe and Latin America, we’re building The Playstack - the technology powering the next generation of sports, gaming, and fan experiences. Join us, and help make it the most widely used platform in the world! From operations, to marketing, to product, we are looking for talented people who will shape how millions of customers play, watch, and connect every day.

As a Staff Machine Learning Engineer in the Applied ML & Research team, you'll drive the development of machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily. This role blends hands-on technical work with strategic thinking — you'll lead by example, contribute high-quality code, and help shape the ML roadmap through cross-functional collaboration.

What the role involves

• Identify high-impact ML opportunities and influence stakeholders to prioritise and support these initiatives
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders
• Set and guide technical direction across ML projects, ensuring alignment with technical best practices and business goals
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement

What we are looking for

• Master's degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field
• 7+ years of industry experience building and deploying ML models at scale
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy
• Proficiency in Python (with libraries such as PyTorch, XGBoost, and Scikit-learn) and SQL
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies
• A track record of shipping production-level ML products and maintaining high code quality
• Excellent problem-solving skills and the ability to scope and disambiguate complex ML projects into clear, achievable milestones