Senior Machine Learning Engineer

At Viaplay Group, we entertain millions of people every day through our streaming services, radio networks, and TV channels. We believe in the power of content not just as a way of telling stories and touching lives, but also expanding worlds. We're looking for the best people to join us on our journey. Right now, we're searching for someone with extensive experience as a Machine Learning Engineer or Data Scientist to join our team in the Stockholm office – are you ready to hit play on an exciting career change? The Role As a Senior Machine Learning Engineer at Viaplay, you will play a key role in shaping the future of our data-driven products and services. Operating at the intersection of personalization, customer engagement, and operational intelligence, you'll lead the design and deployment of cutting-edge ML systems that directly impact millions of users. Your work will extend beyond model development, you'll serve as a strategic partner to stakeholders across product, tech, and commercial teams, translating business objectives into scalable ML solutions. From enhancing recommendation engines to unlocking deep user insights and enabling smarter decision-making across the organization, your contributions will be foundational to Viaplay's continued growth and innovation. You'll join a high-performing, inclusive, and curious team of ML Engineers, Data Scientists, Analysts, and Data Engineers. Together, we foster a culture of experimentation, shared ownership, and technical excellence. Our recommender system is already powering engaging user experiences, and this is only the beginning of our machine learning journey. What You'll Be Doing Design, develop, and deploy scalable machine learning models for recommendation, prediction, and classification tasks Perform advanced statistical analyses to uncover trends in user behavior, content performance, and platform usage Translate business challenges into structured machine learning problems and deliver measurable solutions Collaborate cross-functionally with product, engineering, content, commercial, and marketing teams to drive strategic, data-informed decisions Build robust, production-ready pipelines and monitor ML systems in a cloud-native environment Contribute to a culture of experimentation, innovation, and continuous learning within the data team Take ownership and lead end-to-end machine learning projects, from ideation and stakeholder alignment to development, deployment, and post-launch evaluation Lead Data scientist/Machine learning engineer forum What we're looking for We understand you may feel confident ticking certain boxes more than others and that's why we always keep an open mind in our recruitment process. But, in order to thrive in this role, we do believe you'll have at least some experience in the following: Proven experience leading machine learning projects end-to-end, from problem definition and stakeholder alignment to deployment and performance monitoring in production 7+ years of hands-on experience in applied Machine Learning or Data Science within production environments Deep understanding of statistical modeling, machine learning algorithms, and their real-world applications Advanced programming skills in Python, along with solid experience in SQL and Apache Spark Solid experience deploying ML models in cloud environments (preferably AWS) using containerization tools like Docker Familiarity with orchestration tools such as Airflow, enabling reliable ML pipeline execution and monitoring Strong communication skills, with the ability to clearly explain complex technical concepts to both technical and non-technical stakeholders Demonstrated ability to build ML systems that are scalable, robust, and explainable, not just performant in isolation. Comfortable working in cross-functional, agile teams where priorities shift quickly and collaboration is key Strong academic background in Statistics, Mathematics, Computer Science, or another quantitative field It's a bonus if you have: Experience working on recommender systems, personalization, or content optimization — ideally in media, streaming, or e-commerce domains Familiarity with MLOps practices, including CI/CD for ML, model monitoring, and observability in production Knowledge of experimentation platforms (e.g., A/B testing infrastructure) and causal inference techniques Exposure to real-time or near-real-time ML architectures and streaming data systems

At Viaplay Group, we entertain millions of people every day through our streaming services, radio networks, and TV channels. We believe in the power of content not just as a way of telling stories and touching lives, but also expanding worlds.