Machine Learning Engineer

At the Riot Singapore Efficiency team, we're driven by innovation and optimization. Our team harnesses the power of Machine Learning approaches and Artificial Intelligence to reimagine the way we build games. By leveraging cutting-edge technology, we aim to streamline development processes, enhance creative workflows, which finally deliver immersive experiences to players around the globe. Join us in pushing the boundaries of what's possible in game development and shaping the future of interactive entertainment. As a Machine Learning Engineer, you will help apply Machine Learning techniques to support efficiency in our game development processes. You will collaborate closely with game teams to assist in speeding up creative pipelines, streamlining 2D art and 3D modeling workflows and introducing new tools aimed at improving player experiences. Responsibilities Collaborate on building and implementing Machine Learning models aimed at accelerating creative pipeline optimizations, enhancing processes such as content creation, iteration, and integration. Work closely with game designers, engineers, and artists to identify opportunities for applying Machine Learning techniques to streamline creative workflows, improve asset generation, and enhance overall efficiency in game development. Conduct experiments to validate models, measure their effectiveness and support continuous improvement. Stay informed about recent developments in Machine Learning and gaming technology to explore new methods for enhancing creative workflows. Required Skills Master’s degree in Computer Science, Statistics, Mathematics, or a related field, with a focus on Machine Learning, Data Science, or Artificial Intelligence. At least 1 or 2 years of experience applying Data Science techniques to real-world problems, demonstrating successful data-driven solutions. Comfortable in programming languages such as Python, R, C, C++, C#, and familiar with libraries and frameworks such as TensorFlow, PyTorch, or JAX. Solid understanding of statistical analysis, experimental design, and data visualization techniques. Exposure or interest in Generative Models, such as Score-based Diffusion Models, Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs). Strong communication skills and ability to clearly explain technical concepts to non-technical audiences. Desired Skills Experience working in the gaming industry or a related field is a plus. For this role, you'll find success through craft expertise, a collaborative spirit, and decision-making that prioritizes the delight of players. We will be looking at your academic background, experience, and your personal relationship with games. If you embody player empathy and care about players' experiences, this could be your role!