Machine Learning Researcher

As a Machine Learning Researcher at Suno, you will be at the forefront of the generative audio revolution, developing the core models that allow anyone to create high-fidelity music from text. You will work on the intersection of large-scale transformer architectures and advanced audio synthesis to improve musicality, structure, and vocal quality. Your work will directly impact millions of users by pushing the state-of-the-art in long-form coherence and multi-modal alignment.

- Design and implement novel architectures for controllable, high-fidelity music generation. - Scalably train large-scale generative models using distributed computing frameworks. - Investigate and optimize techniques for long-context temporal modeling to improve song structure and consistency. - Collaborate with the engineering team to deploy research breakthroughs into production-ready inference pipelines. - Conduct rigorous evaluations of model performance using both objective metrics and subjective human-in-the-loop feedback. - Monitor and stay ahead of the latest developments in LLMs and audio research to maintain Suno's competitive edge. - Contribute to the internal research roadmap and mentor junior researchers/engineers.

- PhD or equivalent industry experience in Machine Learning, Computer Science, or a related quantitative field. - Proven track record of publishing at top-tier conferences such as NeurIPS, ICML, ICLR, or CVPR. - Deep expertise in generative modeling, specifically with Diffusion Models, Transformers, or VAEs applied to high-dimensional data. - Strong proficiency in PyTorch and experience training models on large-scale GPU clusters. - Familiarity with Digital Signal Processing (DSP) and the nuances of raw audio or spectrogram-based synthesis. - Ability to work independently in a fast-paced, remote-first research environment and communicate complex technical concepts effectively. - Experience with large-scale data curation and quality filtering for multi-modal datasets.