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Machine Learning Scientist

SunoBoston
On-site Full-time

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Experience Level

Mid to Senior

Qualifications

Minimum of 5 years of experience in training state-of-the-art models using distributed PyTorch frameworks. In-depth knowledge of the full data engineering stack, including the design, training, and evaluation of machine learning models. Demonstrable track record of independently leading research projects from inception to completion. Extensive experience in training large generative models from scratch, including LLMs or diffusion models. A genuine passion for music—whether listening, exploring, or creating—is a significant advantage. A Bachelor’s degree or equivalent in a relevant field is required.

About the job

Join the Innovative Team at Suno

At Suno, we are not just a music company; we are a creative powerhouse dedicated to igniting imagination through the magic of sound. Harnessing the capabilities of the world’s most advanced AI music model, we provide an exceptional creative platform, including our groundbreaking generative audio workstation, Suno Studio. We believe in empowering everyone—from shower singers to seasoned artists—to create, share, and discover music, making musical expression accessible to all.

Role Overview

We are seeking passionate early members of our research team to collaborate closely with our founding members. You will play a pivotal role in shaping the future of our machine learning initiatives by making critical technical decisions on the development and deployment of our cutting-edge ML models, boasting an H100/scientist ratio exceeding 100x.

Explore the Suno job posting here!

About Suno

Suno stands at the intersection of technology and artistry, crafting a platform where creativity knows no bounds. Our cutting-edge AI music model not only transforms the way individuals interact with music but also fosters a global community that celebrates musical expression. By joining Suno, you become part of a visionary team that is dedicated to revolutionizing the music landscape.

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