// Open Role at Riot Games
Join Riot Games' Singapore Efficiency team as a Staff Machine Learning Engineer, Audio, to develop innovative tools that streamline audio production, empowering sound designers to focus on creative craft.
Riot Games was established in 2006 by entrepreneurial gamers who believe that player-focused game development can result in great games. In 2009, Riot released its debut title League of Legends to critical and player acclaim. As the most played PC game in the world, over 100 million play every month. Players form the foundation of our community and it’s for them that we continue to evolve and improve the League of Legends experience.
We’re looking for humble but ambitious, razor-sharp professionals who can teach us a thing or two. We promise to return the favor. Like us, you take play seriously; you’re passionate about games. We embrace those who see things differently, aren’t afraid to experiment, and who have a healthy disregard for constraints.
That's where you come in.
Riot’s Singapore Efficiency team builds the technology that lets our creative teams do their best work. In audio, a lot of a sound designer’s day goes to repetitive editing, processing, and asset management rather than to the sound design itself, and that is the gap we want to close. You will be reporting to the Senior Manager, Machine Learning Engineer.
As a Staff Machine Learning Engineer, Audio, you’ll build tools that handle the tedious and technical parts of audio production so sound designers and audio teams can focus on the craft of sound.
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 past studies, experience, and your personal relationship with games. If you embody player empathy and care about players' experiences, this could be your role!
Master’s or Ph.D. degree in Computer Science, Statistics, Mathematics, or a related field, with a focus on Machine Learning, Data Science, or Artificial Intelligence. Audio specialization: Deep, proven expertise in audio ML, covering generative audio, neural vocoders, text-to-speech/voice conversion, music modeling, and audio representation learning. At least 5 years of experience applying Machine Learning to real-world problems, with a demonstrated record of delivering impactful, end-to-end ML solutions in Audio in a fast-paced environment. Evidence of working on state-of-the-art approaches, demonstrated through peer-reviewed publications and/or shipped projects, open-source contributions, or production systems in Audio that pushed the technical frontier. Proficiency in programming languages such as Python, C, C++, or C#, and strong hands-on experience with frameworks such as PyTorch, TensorFlow, or JAX. Strong understanding of statistical analysis, experimental design, and evaluation techniques. Deep, hands-on experience with Generative Models, including audio diffusion and flow matching models, VAEs, and autoregressive/transformer models for waveform or spectrogram modeling, applied to building artist-assistive tools and accelerating technical steps in the audio pipeline. Excellent communication skills, with the ability to effectively communicate complex technical concepts to non-technical stakeholders. Leadership experience, including mentoring junior team members and interns and driving cross-functional collaboration, is highly desirable.