Senior Machine Learning Engineer - Applied ML & Research

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 Senior Machine Learning Engineer in our Applied ML & Research team, you’ll drive the development of cutting-edge 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.

You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration., * Partner with product and engineering to identify and execute machine learning use cases that deliver measurable impact

• Design, build, and iterate on machine learning solutions (e.g., classifiers, regressors, ranking/retrieval, and rule-based components)
• Contribute across the ML lifecycle: data exploration, feature engineering, training, evaluation, deployment, and monitoring
• Implement reliable training/inference pipelines and help improve reproducibility, testing, and observability
• Communicate model behavior, trade-offs, and results clearly to both technical and non-technical stakeholders
• Contribute to team standards: code quality, documentation, experimentation hygiene, and responsible ML practices

Do you have a Master’s degree?, * Bachelor’s degree in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field (Master’s a plus)

• 4+ years of industry experience building and deploying ML systems
• Solid proficiency in Python and familiarity with common ML libraries (e.g., PyTorch, XGBoost) and SQL
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies
• Demonstrated ability to write maintainable, tested code, participate in code reviews, and follow engineering best practices
• Strong problem-solving skills with the ability to break down ambiguous problems into scoped tasks and deliver iteratively, * Familiarity with ML tooling such as MLflow, ZenML, or Metaflow
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito)
• Exposure to streaming data platforms like Kafka
• Contributions to open-source ML projects

We are a global technology group, dedicated to building the future of entertainment and fan-centric experiences. With commercial markets in Brazil, Belgium, Poland, Romania, Greece and Serbia, and a network of offices across Spain, Croatia, Malta, Gibraltar, the Netherlands and the UK, we are a truly international organization. Our purpose at Super has evolved from sports and betting into creating the platform that stretches into the wider world of technology-driven entertainment. With a growing and diverse team of more than 5,000 people, we create immersive, responsible, and personalised experiences for millions of customers worldwide.

Shaping the Future of Play

Everything we do at Super is rooted in doing what is right: for customers, for each other, and for our long-term vision. Our Culture Manifesto is our North Star. It captures our purpose, mission, and the six core beliefs that shape how we think, make decisions, and act every day. Want to explore our culture in more detail? Visit our careers page: super.xyz/careers

Super is committed to the highest standards of compliance, safety, and responsibility. As such, we are active members of the International Betting Integrity Association (IBIA) and the European Gaming & Betting Association (EGBA).

At Super, we operate as a high-performing team. We hire and grow talent based on ability and potential, regardless of background and identity because we know diverse perspectives, drive better performance.

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